heavy rainfall events
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2022 ◽  
Vol 22 (1) ◽  
pp. 23-40
Author(s):  
Chung-Chieh Wang ◽  
Pi-Yu Chuang ◽  
Chih-Sheng Chang ◽  
Kazuhisa Tsuboki ◽  
Shin-Yi Huang ◽  
...  

Abstract. In this study, the performance of quantitative precipitation forecasts (QPFs) by the Cloud-Resolving Storm Simulator (CReSS) in Taiwan, at a horizontal grid spacing of 2.5 km and a domain size of 1500×1200 km2, in the range of 1–3 d during three Mei-yu seasons (May–June) of 2012–2014 is evaluated using categorical statistics, with an emphasis on heavy-rainfall events (≥100 mm per 24 h). The categorical statistics are chosen because the main hazards are landslides and floods in Taiwan, so predicting heavy rainfall at the correct location is important. The overall threat scores (TSs) of QPFs for all events on day 1 (0–24 h) are 0.18, 0.15, and 0.09 at thresholds of 100, 250, and 500 mm, respectively, and indicate considerable improvements at increased resolution compared to past results and 5 km models (TS < 0.1 at 100 mm and TS ≤ 0.02 at 250 mm). Moreover, the TSs are shown to be higher and the model more skillful in predicting larger events, in agreement with earlier findings for typhoons. After classification based on observed rainfall, the TSs of day − 1 QPFs for the largest 4 % of events by CReSS at 100, 250, and 500 mm (per 24 h) are 0.34, 0.24, and 0.16, respectively, and can reach 0.15 at 250 mm on day 2 (24–48 h) and 130 mm on day 3 (48–72 h). The larger events also exhibit higher probability of detection and lower false alarm ratio than smaller ones almost without exception across all thresholds. With the convection and terrain better resolved, the strength of the model is found to lie mainly in the topographic rainfall in Taiwan rather than migratory events that are more difficult to predict. Our results highlight the crucial importance of cloud-resolving capability and the size of fine mesh for heavy-rainfall QPFs in Taiwan.


MAUSAM ◽  
2021 ◽  
Vol 64 (4) ◽  
pp. 625-644
Author(s):  
ASHOK KUMARDAS ◽  
SURINDER KAUR

egkunh ds csflu esa 2009 o 2010 ds ck<+ ds ekSle ds nkSjku micsfluokj o"kkZ ds iwokZuqeku rFkk 2010 esa ck<+ ds ekSle ds le; izpkyukRed ¼9 fd-eh- × 9 fd-eh-½ fun'kZ ¼vkb-Z ,e- Mh-½ dk vkdyu djus ds fy, Hkkjr ekSle foKku foHkkx  ¼vkb-Z ,e- Mh-½ ds izpkyukRed cgq&fun'kZ bUlSacy ¼,e-,e-bZ-½  ¼27 fd-eh- × 27 fd-eh-½ ds vk/kkj ij o"kkZ ds iwokZuqeku dk mi;ksx fd;k x;k gSA micsflu Lrj ij ,e-,e-bZ- vkSj MCY;w-vkj-,Q- ds dk;Z fu"iknu dk foLr`r v/;;u fd;k x;k gSA blls irk pyk gS fd lkekU;r% Hkkjh o"kkZ dh ?kVukvksa dks ekWMyksa }kjk de djds vkdfyr fd;k tkrk gSA  Operational Multi-model Ensemble (MME) (27 km × 27 km) based rainfall forecast of India Meteorological Department (IMD) are utilized to compute rainfall forecast sub-basin wise for Mahanadi basin during flood season 2009 & 2010 and operational WRF (ARW) (9 km × 9 km)  model (IMD) during flood season 2010. The performance of the MME and WRF at the sub-basin level are studied in detail. It is observed that generally heavy rainfall events are under estimated by the models.


2021 ◽  
Author(s):  
Tanja Winterrath ◽  
Ewelina Walawender ◽  
Katharina Lengfeld ◽  
Elmar Weigl

&lt;p&gt;Extreme Niederschlagsereignisse stellen f&amp;#252;r den Katastrophenschutz sowie die Stadt- und Raumplanung eine enorme Herausforderung dar. Nicht zuletzt das verheerende Hochwasser in Nordrhein-Westfalen und Rheinland-Pfalz im Juli 2021 hat in dramatischer Weise gezeigt, wie vulnerabel unsere Gesellschaft gegen&amp;#252;ber extremen Wetterereignissen ist. Dar&amp;#252;ber hinaus deuten Klimaprojektionen darauf hin, dass sich die Anzahl und Intensit&amp;#228;t von Starkregenereignissen in Zukunft weiter erh&amp;#246;hen k&amp;#246;nnten. Als Grundlage f&amp;#252;r gezielte und zukunftsorientierte Ma&amp;#223;nahmen der Klimaanpassung sind Informationen zu H&amp;#228;ufigkeit und Eigenschaften von Starkregenereignissen erforderlich. Die meteorologische Datengrundlage hat der Deutsche Wetterdienst (DWD) in den vergangenen Jahren geschaffen und in diesem Jahr publiziert (Lengfeld et al., 2021).&lt;/p&gt; &lt;p&gt;Im Rahmen des Projekts KlamEx (www.dwd.de/klamex) der Strategischen Beh&amp;#246;rdenallianz &amp;#8222;Anpassung an den Klimawandel&amp;#8220; hat der DWD zusammen mit seinen Partnerbeh&amp;#246;rden einen Katalog erstellt, der alle extremen Niederschlagsereignisse seit 2001 auflistet. Das Besondere daran: die meteorologische Datengrundlage bildet die radarbasierte Niederschlagsklimatologie RADKLIM (www.dwd.de/radklim) des Deutschen Wetterdienstes, so dass f&amp;#252;r jedes Ereignis eine fl&amp;#228;chenhafte Information des Niederschlags vorliegt und dieses somit nicht nur bez&amp;#252;glich einer punktbasierten Intensit&amp;#228;t sondern als Fl&amp;#228;chenobjekt definiert ist.&lt;/p&gt; &lt;p&gt;Die Auswertungen erfolgten f&amp;#252;r elf verschiedene Dauerstufen zwischen einer und 72 Stunden auf einem deutschlandweiten Raster mit einer Gitterl&amp;#228;nge von einem Kilometer. CatRaRE (www.dwd.de/catrare) listet alle unabh&amp;#228;ngigen Ereignisse, deren Niederschlagswert auf einer Mindestfl&amp;#228;che einen definierten Schwellenwert &amp;#252;berschreitet. Als Schwellenwert wurde zum einen die Warnstufe 3 (W3) des Deutschen Wetterdienstes, zum anderen eine statistische Niederschlagsh&amp;#246;he von 5 Jahren (T5) angesetzt. Jedes Ereignis ist &amp;#252;ber den Punkt des maximalen Niederschlags und ein Polynom definiert, das die Fl&amp;#228;che zum Zeitpunkt der maximalen Extremit&amp;#228;t (Eta, eine Kombination aus Fl&amp;#228;che und mittlerer Wiederkehrzeit) des Ereignisses bestimmt. Zus&amp;#228;tzlich enth&amp;#228;lt der Katalog eine Vielzahl geografischer und demografischer Attribute des Ereignisortes, die einen ma&amp;#223;geblichen Einfluss auf die potenzielle Schadwirkung besitzen.&lt;/p&gt; &lt;p&gt;Die Kataloge W3_Eta und T5_Eta sind &amp;#252;ber den Opendata-Server des DWD frei zug&amp;#228;nglich. Zus&amp;#228;tzlich steht eine WebGIS-Anwendung zur Verf&amp;#252;gung.&lt;/p&gt; &lt;p&gt;Im Rahmen dieses Beitrags stellen wir die Kataloge vor, pr&amp;#228;sentieren statistische Auswertungen zum raumzeitlichen Auftreten von Starkregen und zeigen erste Ideen zur Nutzung der Daten im Rahmen der Erstellung deutschlandweiter Hinweiskarten zur Starkregengef&amp;#228;hrdung. &amp;#160;&lt;/p&gt; &lt;p&gt;Lengfeld, Katharina; Walawender, Ewelina; Winterrath, Tanja; Becker, Andreas: CatRaRE: A Catalogue of radar-based heavy rainfall events in Germany derived from 20 years of data, Meteorologische Zeitschrift, 2021, DOI: 10.1127/metz/2021/1088.&lt;/p&gt;


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.


MAUSAM ◽  
2021 ◽  
Vol 63 (4) ◽  
pp. 615-622
Author(s):  
G.K. DAS ◽  
S.N. ROY ◽  
S.K. MIDYA

In this paper an attempt has been made to study climatological characteristics and forecasting aspects of heavy rainfall over Kolkata for data of 34 years of period from 1974 to 2007. Total 184 events has been found out and the data set has been subjected to various types of analysis along with favourable synoptic system and critical index for occurrence of heavy rainfall over Kolkata. Average occurrence is found as 5.4 events per year. Monthly distribution shows maximum of 26% events in July followed by September 20%, August17% and June as 14%. Seasonal distribution naturally indicates maximum of 77% occurrence during monsoon followed by post-monsoon with 14% and pre-monsoon with 09 %. Synoptic analysis revealed that majority of heavy rainfall events occurred due to low pressure system (LPS). Study of 167 cases (during June to October) suggests that when any one of the favourable synoptic condition prevailed over the region and DPD-Wind-PW-WS index reaches a critical value, heavy to very heavy rain occurred over Kolkata and suburban areas.


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 68 (1) ◽  
pp. 51-66
Author(s):  
K. NAGA RATNA ◽  
MANORAMA MOHANTY

In the present study daily rainfall data for 46 years (1969-2014) was selected for the urban stations and surrounding stations for coastal areas of Coastal Andhra Pradesh (CAP) and inland areas of Telanagana (TEL) and Rayalaseema (RSM).  The statistics such as regression, standard deviation and coefficient of variance, significance test using t-test, Mann-Kandell test were worked out for the entire period for the stations.  The stations were selected on the basis where the period of data is same. The t-test thus performed for all stations showed significance (p < 0.001) in seasonal rainfall (JJAS) for all the stations.  Further z-statistics using Mann-Kandell test was performed that showed significant increase at 95% confidence level for Gannavaram, Machilipatnam and Visakhapatnam along the coast of Andhra Pradesh state. Over Telengana, Hyderabad (Urban centre) an inland station, showed significant increase at 90% level of confidence for extreme heavy rainfall events.  Henceforth, seperate studies for each urban centre (Visakhapatnam, Gannavaram, Machilipatnam and Hyderabad) were done and results showed significant increase in rainfall over urban centres compared to other surrounding stations and the significant increase in rainfall was observed for the coastal stations along Andhra Pradesh coast when compared to inland stations of Telanagana and Rayalaseema.  


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