scholarly journals Continuous rainfall simulation: 2. A regionalized daily rainfall generation approach

2012 ◽  
Vol 48 (1) ◽  
Author(s):  
Rajeshwar Mehrotra ◽  
Seth Westra ◽  
Ashish Sharma ◽  
Ratnasingham Srikanthan
Keyword(s):  
Daily Rainfall ◽  
Rainfall Simulation ◽  
Continuous Rainfall

scholarly journals A Synoptic Weather Typing Approach to Simulate Daily Rainfall and Extremes in Ontario, Canada: Potential for Climate Change Projections

2010 ◽  
Vol 49 (5) ◽  
pp. 845-866 ◽  
Author(s):  
Chad Shouquan Cheng ◽  
Guilong Li ◽  
Qian Li ◽  
Heather Auld
Keyword(s):  
Model Development ◽  
Daily Rainfall ◽  
Simulation Models ◽  
Rainfall Simulation ◽  
Rainfall Events ◽  
Logit Regression ◽  
Synoptic Weather ◽  
Weather Typing ◽  
Synoptic Weather Typing ◽  
Cumulative Logit

Abstract An automated synoptic weather typing and stepwise cumulative logit/nonlinear regression analyses were employed to simulate the occurrence and quantity of daily rainfall events. The synoptic weather typing was developed using principal component analysis, an average linkage clustering procedure, and discriminant function analysis to identify the weather types most likely to be associated with daily rainfall events for the four selected river basins in Ontario. Within-weather-type daily rainfall simulation models comprise a two-step process: (i) cumulative logit regression to predict the occurrence of daily rainfall events, and (ii) using probability of the logit regression, a nonlinear regression procedure to simulate daily rainfall quantities. The rainfall simulation models were validated using an independent dataset, and the results showed that the models were successful at replicating the occurrence and quantity of daily rainfall events. For example, the relative operating characteristics score is greater than 0.97 for rainfall events with daily rainfall ≥10 or ≥25 mm, for both model development and validation. For evaluation of daily rainfall quantity simulation models, four correctness classifications of excellent, good, fair, and poor were defined, based on the difference between daily rainfall observations and model simulations. Across four selected river basins, the percentage of excellent and good simulations for model development ranged from 62% to 84% (of 20 individuals, 16 cases ≥ 70%, 7 cases ≥ 80%); the corresponding percentage for model validation ranged from 50% to 76% (of 20 individuals, 15 cases ≥ 60%, 6 cases ≥ 70%).

scholarly journals A Synoptic Weather-Typing Approach to Project Future Daily Rainfall and Extremes at Local Scale in Ontario, Canada

2011 ◽  
Vol 24 (14) ◽  
pp. 3667-3685 ◽  
Author(s):  
Chad Shouquan Cheng ◽  
Guilong Li ◽  
Qian Li ◽  
Heather Auld
Keyword(s):  
Daily Rainfall ◽  
Simulation Models ◽  
River Basins ◽  
Weather Type ◽  
Rainfall Simulation ◽  
Climate Data ◽  
Rainfall Events ◽  
Synoptic Weather ◽  
Weather Typing ◽  
Synoptic Weather Typing

Abstract This paper attempts to project possible changes in the frequency of daily rainfall events late in this century for four selected river basins (i.e., Grand, Humber, Rideau, and Upper Thames) in Ontario, Canada. To achieve this goal, automated synoptic weather typing as well as cumulative logit and nonlinear regression methods was employed to develop within-weather-type daily rainfall simulation models. In addition, regression-based downscaling was applied to downscale four general circulation model (GCM) simulations to three meteorological stations (i.e., London, Ottawa, and Toronto) within the river basins for all meteorological variables (except rainfall) used in the study. Using downscaled GCM hourly climate data, discriminant function analysis was employed to allocate each future day for two windows of time (2046–65, 2081–2100) into one of the weather types. Future daily rainfall and its extremes were projected by applying within-weather-type rainfall simulation models together with downscaled future GCM climate data. A verification process of model results has been built into the whole exercise (i.e., statistical downscaling, synoptic weather typing, and daily rainfall simulation modeling) to ascertain whether the methods are stable for projection of changes in frequency of future daily rainfall events. Two independent approaches were used to project changes in frequency of daily rainfall events: method I—comparing future and historical frequencies of rainfall-related weather types, and method II—applying daily rainfall simulation models with downscaled future climate information. The increases of future daily rainfall event frequencies and seasonal rainfall totals (April–November) projected by method II are usually greater than those derived by method I. The increase in frequency of future daily heavy rainfall events greater than or equal to 25 mm, derived from both methods, is likely to be greater than that of future daily rainfall events greater than or equal to 0.2 mm: 35%–50% versus 10%–25% over the period 2081–2100 derived from method II. In addition, the return values of annual maximum 3-day accumulated rainfall totals are projected to increase by 20%–50%, 30%–55%, and 25%–60% for the periods 2001–50, 2026–75, and 2051–2100, respectively. Inter-GCM and interscenario uncertainties of future rainfall projections were quantitatively assessed. The intermodel uncertainties are similar to the interscenario uncertainties, for both method I and method II. However, the uncertainties are generally much smaller than the projection of percentage increases in the frequency of future seasonal rain days and future seasonal rainfall totals. The overall mean projected percentage increases are about 2.6 times greater than overall mean intermodel and interscenario uncertainties from method I; the corresponding projected increases from method II are 2.2–3.7 times greater than overall mean uncertainties.

scholarly journals Continuous rainfall simulation: 1. A regionalized subdaily disaggregation approach

2012 ◽  
Vol 48 (1) ◽  
Author(s):  
Seth Westra ◽  
Rajeshwar Mehrotra ◽  
Ashish Sharma ◽  
Ratnasingham Srikanthan
Keyword(s):  
Rainfall Simulation ◽  
Continuous Rainfall

scholarly journals Simulation of Daily Rainfall Data using Articulated Weather Generator Model for Seasonal Prediction of ENSO-Affected Zones in Indonesia

2020 ◽  
Vol 52 (2) ◽  
pp. 143
Author(s):  
Andung Bayu Sekaranom ◽  
Emilya Nurjani ◽  
Rika Harini ◽  
Andi Syahid Muttaqin
Keyword(s):  
Large Scale ◽  
Southern Oscillation ◽  
Daily Rainfall ◽  
Regression Function ◽  
Seasonal Prediction ◽  
Weather Generator ◽  
Rainfall Data ◽  
Rainfall Simulation ◽  
Rainfall Prediction ◽  
Student’S T

Synthetic rainfall simulation using weather generator models is commonly used as a substitute at locations with incomplete or short rainfall data. It incorporates a method that can be developed into forecasts of future rainfall. This study was designed to modify a rainfall prediction system based on the principles of weather generator models and to test the validity of the modelling results. It processed the data collected from eight rain stations in zones affected by El-Nino Southern Oscillation (ENSO). A large-scale predictor, that is, SST prediction data in the Nino 3.4 region over the Pacific Ocean was used as the influencing variable in projecting rainfall for the following six months after the predefined dates. Rainfall data from weather stations and SST in 1960-2000 were analyzed to identify the effects of ENSO and build a statistical model based on the regression function. Meanwhile, the model was validated using the data from 2001 to 2007 by backtesting six months in a row. The analysis results showed that the model could simulate both low rainfall in the dry season and high one in the rainy season. Validation by the student's t-test confirmed that the six-month synthetic rain data at nearly all observed stations was homogenous. For this reason, the developed model can be potentially used as one of the season prediction systems.  

Improving daily rainfall extremes simulation using the generalized Pareto distribution: a case study in Western Iran

2022 ◽  
Vol 85 ◽  
pp. 193-204
Author(s):  
N Shahraki ◽  
S Marofi ◽  
S Ghazanfari
Keyword(s):  
Spatial Correlation ◽  
Water Resource ◽  
Pareto Distribution ◽  
Generalized Pareto Distribution ◽  
Daily Rainfall ◽  
Information Criterion ◽  
Rainfall Simulation ◽  
Model Order ◽  
Generalized Pareto ◽  
Agricultural Planning

Prediction of the occurrence or non-occurrence of daily rainfall plays a significant role in agricultural planning and water resource management projects. In this study, gamma distribution function (GDF), kernel, and exponential (EXP) distributions were coupled (piecewise) with a generalized Pareto distribution. Thus, the gamma-generalized Pareto (GGP), kernel-generalized Pareto (KGP), and exponential-generalized Pareto (EGP) models were used. The aim of the present study was to introduce new methods to modify the simulated generation of extreme rainfall amounts of rainy seasons based on the preserved spatial correlation. The best approach was identified using the normalized root mean square error (NRMSE) criterion. For this purpose, the 30-yr daily rainfall datasets of 21 synoptic weather stations located in different climates of West Iran were analyzed. The first, second, and third-order Markov chain (MC) models were used to describe rainfall time series frequencies. The best MC model order was detected using the Akaike information criterion and Bayesian information criterion. Based on the best identified MC model order, the best piecewise distribution models, and the Wilks approach, rainfall events were modeled with regard to the spatial correlation among the study stations. The performance of the Wilks approach was verified using the coefficient of determination. The daily rainfall simulation resulted in a good agreement between the observed and the generated rainfall data. Hence, the proposed approach is capable of helping water resource managers in different contexts of agricultural planning.

Stochastic modelling of periodicity and trend for multisite daily rainfall simulation

2008 ◽  
Vol 361 (3-4) ◽  
pp. 319-329 ◽  
Author(s):  
N.T. Kottegoda ◽  
L. Natale ◽  
E. Raiteri
Keyword(s):  
Daily Rainfall ◽  
Stochastic Modelling ◽  
Rainfall Simulation

scholarly journals Sensitivity of CONUS Summer Rainfall to the Selection of Cumulus Parameterization Schemes in NU-WRF Seasonal Simulations

2017 ◽  
Vol 18 (6) ◽  
pp. 1689-1706 ◽  
Author(s):  
Takamichi Iguchi ◽  
Wei-Kuo Tao ◽  
Di Wu ◽  
Christa Peters-Lidard ◽  
Joseph A. Santanello ◽  
...  
Keyword(s):  
Temporal Variation ◽  
Daily Rainfall ◽  
Rainfall Simulation ◽  
Cumulus Parameterization ◽  
Parameterization Schemes ◽  
Shallow Cumulus ◽  
Specific Scheme ◽  
Cumulus Parameterization Schemes ◽  
Mean Square Errors ◽  
Selection Of

Abstract This study investigates the sensitivity of daily rainfall rates in regional seasonal simulations over the contiguous United States (CONUS) to different cumulus parameterization schemes. Daily rainfall fields were simulated at 24-km resolution using the NASA-Unified Weather Research and Forecasting (NU-WRF) Model for June–August 2000. Four cumulus parameterization schemes and two options for shallow cumulus components in a specific scheme were tested. The spread in the domain-mean rainfall rates across the parameterization schemes was generally consistent between the entire CONUS and most subregions. The selection of the shallow cumulus component in a specific scheme had more impact than that of the four cumulus parameterization schemes. Regional variability in the performance of each scheme was assessed by calculating optimally weighted ensembles that minimize full root-mean-square errors against reference datasets. The spatial pattern of the seasonally averaged rainfall was insensitive to the selection of cumulus parameterization over mountainous regions because of the topographical pattern constraint, so that the simulation errors were mostly attributed to the overall bias there. In contrast, the spatial patterns over the Great Plains regions as well as the temporal variation over most parts of the CONUS were relatively sensitive to cumulus parameterization selection. Overall, adopting a single simulation result was preferable to generating a better ensemble for the seasonally averaged daily rainfall simulation, as long as their overall biases had the same positive or negative sign. However, an ensemble of multiple simulation results was more effective in reducing errors in the case of also considering temporal variation.

Sign in / Sign up

Export Citation Format

Share Document