A novel bias correction method for extreme rainfall events based on L‐moments

2021 ◽  
Author(s):  
A. N. Rohith ◽  
K. P. Sudheer
Keyword(s):  
Bias Correction ◽  
Extreme Rainfall ◽  
Correction Method ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Bias Correction Method ◽  
L Moments

scholarly journals Impact of Bias Correction on Present and Mid-Future Rainfall Projections over Senegal

2021 ◽  
Vol 17 (37) ◽  
pp. 137
Author(s):  
Sarr Alioune Badara ◽  
Diatta Samo ◽  
Kébé Ibourahima ◽  
Sultan Benjamin ◽  
Camara Moctar
Keyword(s):  
Bias Correction ◽  
Regional Climate ◽  
Correction Method ◽  
Climate Change Signal ◽  
Linear Scaling ◽  
Rainfall Events ◽  
Mean Intensity ◽  
Bias Correction Method ◽  
The Mean ◽  
The Impact

In this study, we analyze the impact of bias correction models on present and future precipitation and extremes rainfall events over Senegal. The commonly used linear scaling (LS) bias correction method has been applied on four (4) regional climate models (RCMs) of the Coordinated Regional Climate Downscaling Experiment (CORDEX) program. The linear scaling bias correction method was firstly calibrated and validated during the 1976-1990 and 1991-2005 periods, respectively. The comparison with the observed data revealed that the linear scaling method significantly improves the mean and the extreme precipitations during the validation period. The RCMs generally simulate a decrease of rainfall in the mid-twenty-first century under the RCP8.5 greenhouse gas concentration pathway compared to the reference period (1976-2005), except for the CCLM4 and the RCA4 models which show respectively a slight increase overall Senegal and the east of the country. The changes in precipitation indices such as the number of wet days (R1mm) and mean frequency of heavy rainfall events (R20mm) follows that mean precipitation change distribution. Almost uncorrected RCMs (except RCA4) predict during the near future an increase in of the mean intensity of daily rainfall events (SDII), the mean intensity of precipitation events above the 95th Percentile (R95PTOT) and the mean maximum dry spells length (CDD), whereas a decrease in the mean maximum wet spells length (CWD) is projected. After applying the LS bias correction, the spatial distribution patterns are not so much modified in all the models but the magnitude of the climate change signal is either amplified or moderated depending on the considered variables.

scholarly journals C-Band Polarimetric Radar QPE Based on Specific Differential Propagation Phase for Extreme Typhoon Rainfall

2013 ◽  
Vol 30 (7) ◽  
pp. 1354-1370 ◽  
Author(s):  
Yadong Wang ◽  
Jian Zhang ◽  
Alexander V. Ryzhkov ◽  
Lin Tang
Keyword(s):  
Complex Terrain ◽  
Extreme Rainfall ◽  
Correction Method ◽  
Screening Process ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Drop Shape ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Typhoon Rainfall

Abstract To obtain accurate radar quantitative precipitation estimation (QPE) for extreme rainfall events such as land-falling typhoon systems in complex terrain, a new method was developed for C-band polarimetric radars. The new methodology includes a correction method based on vertical profiles of the specific differential propagation phase (VPSDP) for low-level blockage and an optimal relation between rainfall rate () and the specific differential phase (). In the VPSDP-based correction approach, a screening process is applied to fields, where missing or unreliable data from lower tilts caused by severe beam blockage are replaced with data from upper and unblocked tilts. The data from upper tilts are adjusted to account for variations in the vertical profile of . The corrected field is then used for rain-rate estimations. To acquire an accurate QPE result, a new relation for C-band polarimetric radars was derived through simulations using drop size distribution (DSD) and drop shape relation (DSR) observations from typhoon systems in Taiwan. The VPSDP-based correction method with the new relation was evaluated using the typhoon cases of Morakot (2009) and Fanapi (2010).

scholarly journals Development of multi-model ensemble for projection of extreme rainfall events in Peninsular Malaysia

2019 ◽  
Vol 50 (6) ◽  
pp. 1772-1788 ◽  
Author(s):  
Muhammad Noor ◽  
Tarmizi Ismail ◽  
Shamsuddin Shahid ◽  
Mohamed Salem Nashwan ◽  
Shahid Ullah
Keyword(s):  
Bias Correction ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Correction Method ◽  
Peninsular Malaysia ◽  
Model Ensemble ◽  
Random Forest Regression ◽  
Maximum Increase ◽  
Extreme Rainfall Events ◽  
Rainfall Extremes

Abstract Possible changes in rainfall extremes in Peninsular Malaysia were assessed in this study using an ensemble of four GCMs of CMIP5. The performance of four bias correction methods was compared, and the most suitable method was used for downscaling of GCM simulated daily rainfall to the spatial resolution (0.25°) of APHRODITE rainfall. The multi-model ensemble (MME) mean of the downscaled rainfall was developed using a random forest regression algorithm. The MME projected rainfall for four RCPs were compared with APHRODITE rainfall for the base year (1961–2005) to assess the annual and seasonal changes in eight extreme rainfall indices. The results showed power transformation as the most suitable bias correction method. The maximum changes in most of the annual and seasonal extreme rainfall indices were observed for RCP8.5 in the last part of this century. The maximum increase was observed for 1-day and 5 consecutive days' rainfall amount for RCP4.5. Spatial distribution of the changes revealed higher increase of the extremes in the northeast region where rainfall extremes are already very high. The increase in rainfall extremes would increase the possibility of frequent hydrological disasters in Peninsular Malaysia.

Bias Correction Method of High-Resolution Satellite-Based Precipitation Product for Peninsular Malaysia

2021 ◽  
Author(s):  
Zafar Iqbal ◽  
Shamsuddin Shahid ◽  
Kamal Ahmed ◽  
Xiaojun Wang ◽  
Tarmizi Ismail ◽  
...  
Keyword(s):  
High Resolution ◽  
Bias Correction ◽  
Daily Rainfall ◽  
Skill Score ◽  
Correction Method ◽  
Peninsular Malaysia ◽  
Rainfall Events ◽  
Bias Correction Method ◽  
Rainfall Occurrence ◽  
Classification And Regression

Abstract Satellite-based precipitation (SBP) is emerging as a reliable source for high-resolution rainfall estimates over the globe. However, uncertainty in SBP is still significant, limiting their use without evaluation and often without bias correction. The bias correction of SBP remained a challenge for atmospheric scientists. In this study, the performance of six SBPs, namely, SM2RAIN-ASCAT, IMERG, GsMap, CHIRPS, PERSIANN-CDS and PERSIANN-CSS in replicating observed daily rainfall at 364 stations over Peninsular Malaysia was evaluated. The bias of the most suitable SBP was corrected using a novel machine learning (ML)-based bias-correction method. The proposed bias-correction method consists of an ML classifier to correct the bias in estimating rainfall occurrence and an ML regression model to correct the amount of rainfall during rainfall events. The performance of different widely used ML algorithms for classification and regression were evaluated to select the suitable algorithms. IMERG showed better performance, showing a higher correlation coefficient (R2) of 0.57 and Kling-Gupta Efficiency (KGE) of 0.5 compared to the other products. The performance of random forest (RF) was better than the k-nearest neighbourhood (KNN) for both classification and regression. RF classified the rainfall events with a skill score of 0.38 and estimated the rainfall amount of a rainfall event with the modified Index of Agreement (md) of 0.56. Comparison of IMERG and bias-corrected IMERG (BIMERG) revealed an average reduction in RMSE by 55% in simulating observed rainfall. The proposed bias correction method performed much better when compared with the conventional bias correction methods such as linear scaling and quantile regression. The BIMERG could reliably replicate the spatial distribution of heavy rainfall events, indicating its potential for hydro-climatic studies like flood and drought monitoring in the study area.

The Influence of Geographical Factors on Extreme Rainfalls in Lampung Province

2019 ◽  
Vol 1 (1) ◽  
pp. 33
Author(s):  
M Welly
Keyword(s):  
Geographical Location ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Design Flood ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Ungauged Basin ◽  
Flood Discharge ◽  
Design Rainfall ◽  
Geographical Factors

Many people in Indonesia calculate design rainfall before calculating the design flooddischarge. The design rainfall with a certain return period will eventually be convertedinto a design flood discharge by combining it with the characteristics of the watershed.However, the lack of a network of rainfall recording stations makes many areas that arenot hydrologically measured (ungauged basin), so it is quite difficult to know thecharacteristics of rain in the area concerned. This study aims to analyze thecharacteristics of design rainfall in Lampung Province. The focus of the analysis is toinvestigate whether geographical factors influence the design rainfall that occurs in theparticular area. The data used in this study is daily rainfall data from 15 rainfallrecording stations spread in Lampung Province. The method of frequency analysis usedin this study is the Gumbel method. The research shows that the geographical location ofan area does not have significant effect on extreme rainfall events. The effect of risingearth temperatures due to natural exploitation by humans tends to be stronger as a causeof extreme events such as extreme rainfall.Keywords: Influence, geographical, factors, extreme, rainfall.

CLIMATOLOGY OF EXTREME RAINFALL EVENTS IN EASTERN AND NORTHERN SANTA CATARINA STATE, BRAZIL: PRESENT AND FUTURE CLIMATE

2013 ◽  
Vol 31 (3) ◽  
pp. 413 ◽  
Author(s):  
André Becker Nunes ◽  
Gilson Carlos Da Silva
Keyword(s):  
Climate Model ◽  
Climate Modeling ◽  
Linear Trend ◽  
Extreme Rainfall ◽  
Eastern Region ◽  
Santa Catarina ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
History Of ◽  
Santa Catarina State

ABSTRACT. The eastern region of Santa Catarina State (Brazil) has an important history of natural disasters due to extreme rainfall events. Floods and landslides are enhancedby local features such as orography and urbanization: the replacement of natural surface coverage causing more surface runoff and, hence, flooding. Thus, studies of this type of events – which directly influence life in the towns – take on increasing importance. This work makes a quantitative analysis of occurrences of extreme rainfall events in the eastern and northern regions of Santa Catarina State in the last 60 years, through individual analysis, considering the history of floods ineach selected town, as well as an estimate through to the end of century following regional climate modeling. A positive linear trend, in most of the towns studied, was observed in the results, indicating greater frequency of these events in recent decades, and the HadRM3P climate model shows a heterogeneous increase of events for all towns in the period from 2071 to 2100.Keywords: floods, climate modeling, linear trend. RESUMO. A região leste do Estado de Santa Catarina tem um importante histórico de desastres naturais ocasionados por eventos extremos de precipitação. Inundações e deslizamentos de terra são potencializados pelo relevo acidentado e pela urbanização das cidades da região: a vegetação nativa vem sendo removida acarretando um maior escoamento superficial e, consequentemente, em inundações. Desta forma, torna-se de suma importância os estudos acerca deste tipo de evento que influencia diretamente a sociedade em geral. Neste trabalho é realizada uma análise quantitativa do número de eventos severos de precipitação ocorridos nas regiões leste e norte de Santa Catarina dos últimos 60 anos, por meio de uma análise pontual, considerandoo histórico de inundações de cada cidade selecionada, além de uma projeção para o fim do século de acordo com modelagem climática regional. Na análise dos resultados observou-se uma tendência linear positiva na maioria das cidades, indicando uma maior frequência deste tipo de evento nas últimas décadas, e o modelo climático HadRM3P mostra um aumento heterogêneo no número de eventos para todas as cidades no período de 2071 a 2100.Palavras-chave: inundações, modelagem climática, tendência linear.

Comparing vulnerability and institutional capacities in three Latin American coastal localities in response to extreme rainfall events

2021 ◽  
Vol 25 (1) ◽  
Author(s):  
Arturo Ruiz-Luna ◽  
Claudia Martínez-Peralta ◽  
Patricia P. B. Eichler ◽  
Leonardo R. Teixeira ◽  
Montserrat Acosta-Morel ◽  
...  
Keyword(s):  
Latin American ◽  
Extreme Rainfall ◽  
Extreme Rainfall Events ◽  
Rainfall Events

scholarly journals Tropical cyclone contribution to extreme rainfall over southwest Pacific Island nations

2021 ◽  
Author(s):  
Anil Deo ◽  
Savin S. Chand ◽  
Hamish Ramsay ◽  
Neil J. Holbrook ◽  
Simon McGree ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
El Niño ◽  
El Nino ◽  
Extreme Rainfall ◽  
Pacific Island ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Southwest Pacific ◽  
Inactive Phase ◽  
Pacific Island Nations

AbstractSouthwest Pacific nations are among some of the worst impacted and most vulnerable globally in terms of tropical cyclone (TC)-induced flooding and accompanying risks. This study objectively quantifies the fractional contribution of TCs to extreme rainfall (hereafter, TC contributions) in the context of climate variability and change. We show that TC contributions to extreme rainfall are substantially enhanced during active phases of the Madden–Julian Oscillation and by El Niño conditions (particularly over the eastern southwest Pacific region); this enhancement is primarily attributed to increased TC activity during these event periods. There are also indications of increasing intensities of TC-induced extreme rainfall events over the past few decades. A key part of this work involves development of sophisticated Bayesian regression models for individual island nations in order to better understand the synergistic relationships between TC-induced extreme rainfall and combinations of various climatic drivers that modulate the relationship. Such models are found to be very useful for not only assessing probabilities of TC- and non-TC induced extreme rainfall events but also evaluating probabilities of extreme rainfall for cases with different underlying climatic conditions. For example, TC-induced extreme rainfall probability over Samoa can vary from ~ 95 to ~ 75% during a La Niña period, if it coincides with an active or inactive phase of the MJO, and can be reduced to ~ 30% during a combination of El Niño period and inactive phase of the MJO. Several other such cases have been assessed for different island nations, providing information that have potentially important implications for planning and preparing for TC risks in vulnerable Pacific Island nations.

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