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.  

scholarly journals Dominant Synoptic Disturbance in the Extreme Rainfall at Cherrapunji, Northeast India, Based on 104 Years of Rainfall Data (1902–2005)

2017 ◽  
Vol 30 (20) ◽  
pp. 8237-8251 ◽  
Author(s):  
Fumie Murata ◽  
Toru Terao ◽  
Hatsuki Fujinami ◽  
Taiichi Hayashi ◽  
Haruhisa Asada ◽  
...  
Keyword(s):  
Bay Of Bengal ◽  
North Pacific ◽  
Western North Pacific ◽  
Large Scale ◽  
Monsoon Season ◽  
Daily Rainfall ◽  
Northeast India ◽  
Rainfall Data ◽  
Total Rainfall ◽  
Orographic Rainfall

Abstract The characteristics of active rainfall spells (ARSs) at Cherrapunji, northeast India, where extreme high rainfall is experienced, and their relationships with large-scale dynamics were studied using daily rainfall data from 1902 to 2005 and Japanese 55-Year Reanalysis from 1958 to 2005. Extreme high daily rainfalls occur in association with ARSs. The extremely large amounts of rainfall in the monsoon season are determined by the cumulative rainfall during ARSs. ARSs start when anomalous anticyclonic circulation (AAC) at 850 hPa propagates westward from the South China Sea and western North Pacific, and covers the northern Bay of Bengal. The AAC propagates farther westward and suppresses convection over central India during ARSs at Cherrapunji, and continues for 3 to 14 days. Consequently, a northward shift of the monsoon trough during the “break” in the Indian core region occurs. The westerly wind, which prevails in the northern portion of the AAC, transports moisture toward northeast India and enhances moisture convergence over northeast India with southerly moisture transport from the Bay of Bengal, and greatly intensifies the orographic rainfall. In the upper troposphere, the Tibetan high tends to extend southward with the onset of ARSs. A linear relationship can be seen between the length and total rainfall of an ARS. Longer ARSs tend to result in greater total rainfall. AACs with a greater zonal scale tend to produce longer and more intense ARSs. This study provides evidence for the effect of western North Pacific AACs on the Indian summer monsoon.

Prediction of Rainfall in Da-Dong-Yong Hydrologic Station Based on Wavelet Neural Network

2013 ◽  
Vol 726-731 ◽  
pp. 3279-3282 ◽  
Author(s):  
Xiu Li Sang ◽  
Yu Zhen Su ◽  
Han Jie Xiao ◽  
Hua Wang ◽  
Jian Xin Xu
Keyword(s):  
Neural Network ◽  
Prediction Accuracy ◽  
Daily Rainfall ◽  
Wavelet Neural Network ◽  
Rainfall Data ◽  
Rainfall Prediction ◽  
Daily Rainfall Data ◽  
Rainfall Forecast ◽  
Forecast Data ◽  
Accuracy And Stability

This paper aims to seek a way for improving rainfall prediction accuracy from the perspective of time unit points which are 5 days, 10 days, 15 days, 20 days, 25 days, and 30 days. Based on the daily rainfall data from 2001 to 2010 of Da-dong-yong hydrologic station, the rainfalls are predicted by establishing the model of wavelet neural network. Results show that prediction accuracy and stability of time unit points is 30-day > 25-day > 15-day > 10-day > 20-day > 5-day. The trend of six kinds of rainfall forecast is consistent. When the number of forecast data is fewer and time unit point is longer, the accuracy and stability of rainfall forecast are better.

scholarly journals Rainfall Prediction with AMSR-E Soil Moisture Products Using SM2RAIN and Nonlinear Autoregressive Networks with Exogenous Input (NARX) for Poorly Gauged Basins: Application to the Karkheh River Basin, Iran

2018 ◽  
Author(s):  
Majid Fereidoon ◽  
Manfred Koch
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Land Surface ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Rainfall Prediction ◽  
Nonlinear Autoregressive ◽  
Karkheh River Basin ◽  
The Karkheh River ◽  
Narx Model

Accurate estimates of daily rainfall are essential for understanding and modeling the physical processes involved in the interaction between the land surface and the atmosphere. In this study, daily satellite soil moisture observations from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) generated by implementing the standard NASA- algorithm are employed for estimating rainfall, firstly, through the use of recently developed approach, SM2RAIN (Brocca et al., 2013) and, secondly, the nonlinear autoregressive network with exogenous inputs (NARX) neural modelling at five climate stations in the Karkheh river basin (KRB), located in southwest Iran. In the SM2RAIN method, the period 1 January 2003 to 31 December 2005 is used for the calibration of algorithm and the remaining 9 months from 1 January 2006 to 30 September 2006 is used for the validation of the rainfall estimates. In the NARX model, the full study period is split into a training (1 January 2003 to 31 September 2005) and a testing (1 September 2005 to 30 September 2006) stage. For the prediction of the rainfall as the desired target (output), relative soil moisture changes from AMSR-E and measured air temperature time series are chosen as exogenous (external) inputs in NARX. The quality of the estimated rainfall data is evaluated by comparing it with observed rainfall data at the five rain gauges in terms of the correlation coefficient R, the RMSE and the statistical bias. For the SM2RAIN method, R ranges between 0.44 and 0.9 for all stations, whereas for the NARX- model the values are generally slightly lower. Moreover, the values of the bias for each station indicate that although SM2RAIN is likely to underestimate large rainfall intensities, due to the known effect of soil moisture saturation, its biases are somewhat lower than those of NARX. In conclusion, the results of the present study show that with the use of AMSR-E soil moisture products in the physically based SM2RAIN- algorithm as well as in the NARX neural network, rainfall for poorly gauged regions can be fairly predicted.

scholarly journals Influence of oceanic-atmospheric interactions on extreme events of daily rainfall in the Sub-basin 39 located in Northeastern Brazil

RBRH ◽  
2016 ◽  
Vol 21 (4) ◽  
pp. 685-693 ◽  
Author(s):  
João Hipólito Paiva de Britto Salgueiro ◽  
Suzana Maria Gico Lima Montenegro ◽  
Eber José de Andrade Pinto ◽  
Bernardo Barbosa da Silva ◽  
Werônica Meira de Souza ◽  
...  
Keyword(s):  
Extreme Events ◽  
Southern Oscillation ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Daily Rainfall ◽  
Northeastern Brazil ◽  
Geographical Position ◽  
Student’S T ◽  
Atmospheric Systems ◽  
Student’S T Test

ABSTRACT Changes in extreme precipitation have been observed in regions where frequent rainfalls occur over short periods of time followed by prolonged droughts, creating, as a result, new watershed scenarios. Recent studies have attributed such occurrences to possible climate changes. This paper analyzes the correlation between extreme events recorded in the Sub-basin 39, located in Northeastern Brazil, and the anomalies caused by sea surface temperature - SST and the atmospheric systems operating in the region. Pearson correlation coefficients have been used combined with the variables analyzed. For such, trends in precipitation have been obtained by using the method of least squares together with linear regression and the Student's t test. The results obtained have demonstrated that due to the geographical position of the region investigated, both maximum extreme events (areas with positive trends) and minimum extreme events (areas with negative trends) are more dependent on the Dipole Atlantic than on the effects of El Niño Southern Oscillation - ENOS.

scholarly journals Drivers and Impacts of Atmospheric Rivers in New Zealand

2021 ◽  
Author(s):  
◽  
Daemon Kennett
Keyword(s):  
New Zealand ◽  
Water Vapour ◽  
Large Scale ◽  
Southern Oscillation ◽  
Daily Rainfall ◽  
Seasonal Forecasts ◽  
Positive Role ◽  
Atmospheric Rivers ◽  
Synoptic Conditions ◽  
Climate Patterns

<p><b>Atmospheric Rivers (ARs) are long, narrow jets of intense water vapour flux that are a fundamental component of the global atmospheric circulation, transporting moisture and heat from the tropics to higher latitudes. When an AR makes landfall, especially in areas of steep topography, it releases much of its water vapour as precipitation through orographic uplift. Thus, although ARs play a positive role in the distribution and maintenance of water resources in the mid-latitudes, they are also associated with extreme precipitation and flooding. AR events in New Zealand have had major socio-economic consequences with losses to property, farmland, stock, roads and bridges. However, despite knowledge of their occurrence, focused investigations of ARs in New Zealand have received relatively little scientific attention. In particular, little is known about how large-scale climate patterns, such as the Southern Annular Mode (SAM) and El Niño-Southern Oscillation (ENSO), influence ARs and AR-related precipitation extremes.</b></p> <p>The aim of this study is to quantify the impacts and large-scale drivers of AR landfalls in New Zealand. We employ a new AR detection algorithm, developed specifically for the New Zealand case, to investigate landfalling ARs over a 41-year period from 1979-2019. We investigate the general climatology of ARs, and evaluate the synoptic conditions that drive these events. Using a comprehensive daily rainfall dataset comprising 189 stations, we also investigate the impacts of ARs on NZ rainfall and flooding events. For northern and western regions, over 45% of rainfall fell directly under AR conditions, contributing to daily rainfall totals 2.5 times higher on average compared to non-AR days. Further, we find that AR days were associated with up to 70% of daily rainfall totals above the 99th percentile, with insurance damages exceeding NZ $1.4 billion since 1980.</p> <p>Finally, for the first time in New Zealand, we investigate how large-scale climate patterns influence the occurrence of ARs. We find that changes in the leading modes of climate variability can alter seasonal and regional AR frequency by upwards of 30%. The SAM is identified as the dominant driver of AR activity (other than the seasonal cycle), with the positive SAM phase associated with a 16% reduction in AR occurrence during summer (30-35% reduction for the North Island). The links between AR occurrence and ENSO were less clear, though a few statistically significant relationships were found. The Madden-Julian Oscillation (MJO), the leading mode of intraseasonal tropical variability, was found to significantly influence the frequency and timing of AR landfalls (particularly for the northern North Island). Favourable MJO phases were associated with positive AR frequency anomalies +60% above the mean. These results demonstrate potential use of the AR framework in skilful subseasonal-to-seasonal forecasts of extreme rainfall in New Zealand.</p>

scholarly journals Drivers and Impacts of Atmospheric Rivers in New Zealand

2021 ◽  
Author(s):  
◽  
Daemon Kennett
Keyword(s):  
New Zealand ◽  
Water Vapour ◽  
Large Scale ◽  
Southern Oscillation ◽  
Daily Rainfall ◽  
Seasonal Forecasts ◽  
Positive Role ◽  
Atmospheric Rivers ◽  
Synoptic Conditions ◽  
Climate Patterns

<p><b>Atmospheric Rivers (ARs) are long, narrow jets of intense water vapour flux that are a fundamental component of the global atmospheric circulation, transporting moisture and heat from the tropics to higher latitudes. When an AR makes landfall, especially in areas of steep topography, it releases much of its water vapour as precipitation through orographic uplift. Thus, although ARs play a positive role in the distribution and maintenance of water resources in the mid-latitudes, they are also associated with extreme precipitation and flooding. AR events in New Zealand have had major socio-economic consequences with losses to property, farmland, stock, roads and bridges. However, despite knowledge of their occurrence, focused investigations of ARs in New Zealand have received relatively little scientific attention. In particular, little is known about how large-scale climate patterns, such as the Southern Annular Mode (SAM) and El Niño-Southern Oscillation (ENSO), influence ARs and AR-related precipitation extremes.</b></p> <p>The aim of this study is to quantify the impacts and large-scale drivers of AR landfalls in New Zealand. We employ a new AR detection algorithm, developed specifically for the New Zealand case, to investigate landfalling ARs over a 41-year period from 1979-2019. We investigate the general climatology of ARs, and evaluate the synoptic conditions that drive these events. Using a comprehensive daily rainfall dataset comprising 189 stations, we also investigate the impacts of ARs on NZ rainfall and flooding events. For northern and western regions, over 45% of rainfall fell directly under AR conditions, contributing to daily rainfall totals 2.5 times higher on average compared to non-AR days. Further, we find that AR days were associated with up to 70% of daily rainfall totals above the 99th percentile, with insurance damages exceeding NZ $1.4 billion since 1980.</p> <p>Finally, for the first time in New Zealand, we investigate how large-scale climate patterns influence the occurrence of ARs. We find that changes in the leading modes of climate variability can alter seasonal and regional AR frequency by upwards of 30%. The SAM is identified as the dominant driver of AR activity (other than the seasonal cycle), with the positive SAM phase associated with a 16% reduction in AR occurrence during summer (30-35% reduction for the North Island). The links between AR occurrence and ENSO were less clear, though a few statistically significant relationships were found. The Madden-Julian Oscillation (MJO), the leading mode of intraseasonal tropical variability, was found to significantly influence the frequency and timing of AR landfalls (particularly for the northern North Island). Favourable MJO phases were associated with positive AR frequency anomalies +60% above the mean. These results demonstrate potential use of the AR framework in skilful subseasonal-to-seasonal forecasts of extreme rainfall in New Zealand.</p>

scholarly journals GiST: A Stochastic Model for Generating Spatially and Temporally Correlated Daily Rainfall Data

2010 ◽  
Vol 23 (22) ◽  
pp. 5990-6008 ◽  
Author(s):  
Guillermo A. Baigorria ◽  
James W. Jones
Keyword(s):  
North Carolina ◽  
Correlation Coefficients ◽  
Daily Rainfall ◽  
Weather Generator ◽  
Rainfall Data ◽  
Random Numbers ◽  
Rainfall Events ◽  
Pairwise Correlation ◽  
Weather Generators ◽  
Weather Stations

Abstract Weather generators are tools that create synthetic daily weather data over long periods of time. These tools have also been used for downscaling monthly to seasonal climate forecasts, from global and regional circulation models to daily values for use as inputs for crop and other environmental models. One main limitation of most weather generators is that they do not take into account the spatial structure of weather. Spatial correlation of daily rainfall is important when one aggregates, for example, simulated crop yields or hydrology in a watershed or region. A method was developed to generate realizations of daily rainfall for multiple sites in an area while preserving the spatial and temporal correlations among sites. A two-step method generates rainfall events at multiple sites followed by rainfall amounts at sites where generated rainfall events occur. The generation of rainfall events was based on a new orthogonal Markov chain for discrete distributions. For generating rainfall amounts, a vector of random numbers (from a uniform distribution), of order equal to the number of locations with rainfall events that were generated to occur in a day, was matrix-multiplied by the corresponding factorized correlation matrix to create spatially correlated random numbers. Elements from the resulting vector were transformed to a gamma distribution using cumulative probability functions for each location and rescaled to rainfall amounts. One study area was located in north-central Florida, where correlated rainfall data were generated for seven weather stations to evaluate its performance versus a widely used single-site weather generator. A second area was in North Carolina, where rainfall was generated for 25 weather stations to evaluate the effects of a larger number of stations in other regions. One thousand yearlong replications of daily rainfall data were generated for each area. Monthly spatial correlations of generated daily rainfall events and amounts among all pairs of weather stations closely matched their observed counterparts. For daily rainfall amounts the correlation coefficients between the observed pairwise correlation coefficients and the ones estimated from synthetic data among weather stations were 0.977 for Florida and 0.964 for North Carolina. The performance of the geospatial–temporal (GiST) weather generator was also analyzed by comparing the distributions of lengths of dry and wet spells, joint probabilities, Markov transitional probabilities, distance decay of correlation functions, and regionwide days without rainfall at any station. Multiannual mean and standard deviation of the number of rainy days per month and mean monthly rainfall were also calculated. All comparisons between observed and generated rainfall events and amounts using the GiST weather generator were highly correlated. The root-mean-square errors of pairwise correlation values ranged from 0.05 to 0.11 for rainfall events and from 0.03 to 0.06 for amounts.

scholarly journals High-Resolution Tropical Channel Model Simulations of Tropical Cyclone Climatology and Intraseasonal-to-Interannual Variability

2019 ◽  
Vol 32 (22) ◽  
pp. 7871-7895 ◽  
Author(s):  
Dan Fu ◽  
Ping Chang ◽  
Christina M. Patricola ◽  
R. Saravanan
Keyword(s):  
Tropical Cyclone ◽  
North Pacific ◽  
Large Scale ◽  
Channel Model ◽  
Southern Oscillation ◽  
Wrf Model ◽  
Seasonal Prediction ◽  
Horizontal Resolution ◽  
North Indian Ocean ◽  
Channel Configuration

Abstract We tailored a tropical channel configuration of the Weather Research and Forecasting (WRF) Model to study tropical cyclone (TC) activity and associated climate variabilities. This tropical channel model (TCM) covers from 30°S to 50°N at 27-km horizontal resolution, with physics parameterizations carefully selected to achieve more realistic simulations of TCs and large-scale climate mean states. We performed 15-member ensembles of retrospective simulations from 1982 to 2016 hurricane seasons. A thorough comparison with observations demonstrates that the TCM yields significant skills in simulating TC activity climatology and variabilities in each basin, as well as TC physical structures. The correlation of the ensemble averaged accumulated cyclone energy (ACE) with observations in the western North Pacific (WNP), eastern North Pacific (ENP), and North Atlantic (NAT) is 0.80, 0.64, and 0.61, respectively, but is insignificant in the north Indian Ocean (NIO). Moreover, the TCM-simulated modulations of El Niño–Southern Oscillation (ENSO) and the Madden–Julian oscillation (MJO) on the large-scale environment and TC genesis also agree well with observations. To examine the TCM’s potential for seasonal TC prediction, the model is used to forecast the 2017 and 2018 hurricane seasons, using bias-corrected sea surface temperatures (SSTs) from the CFSv2 seasonal prediction results. The TCM accurately predicts the hyperactive 2017 NAT hurricane season and near-normal WNP and ENP hurricane seasons when initialized in May. In addition, the TCM accurately predicts TC activity in the NAT and WNP during the 2018 season, but underpredicts ENP TC activity, in association with a poor ENSO forecast.

scholarly journals Downscaling of Daily Rainfall Occurrence over Northeast Brazil Using a Hidden Markov Model

2004 ◽  
Vol 17 (22) ◽  
pp. 4407-4424 ◽  
Author(s):  
Andrew W. Robertson ◽  
Sergey Kirshner ◽  
Padhraic Smyth
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
General Circulation ◽  
Large Scale ◽  
Hidden Markov ◽  
Southern Oscillation ◽  
Daily Rainfall ◽  
Northeast Brazil ◽  
Rainfall Occurrence ◽  
Dry Spell

Abstract A hidden Markov model (HMM) is used to describe daily rainfall occurrence at 10 gauge stations in the state of Ceará in northeast Brazil during the February–April wet season 1975–2002. The model assumes that rainfall occurrence is governed by a few discrete states, with Markovian daily transitions between them. Four “hidden” rainfall states are identified. One pair of the states represents wet-versus-dry conditions at all stations, while a second pair of states represents north–south gradients in rainfall occurrence. The estimated daily state-sequence is characterized by a systematic seasonal evolution, together with considerable variability on intraseasonal, interannual, and longer time scales. The first pair of states are shown to be associated with large-scale displacements of the tropical convergence zones, and with teleconnections typical of the El Niño–Southern Oscillation and the North Atlantic Oscillation. A nonhomogeneous HMM (NHMM) is then used to downscale daily precipitation occurrence at the 10 stations, using general circulation model (GCM) simulations of seasonal-mean large-scale precipitation, obtained with historical sea surface temperatures prescribed globally. Interannual variability of the GCM's large-scale precipitation simulation is well correlated with seasonal- and spatial-averaged station rainfall-occurrence data. Simulations from the NHMM are found to be able to reproduce this relationship. The GCM-NHMM simulations are also able to capture quite well interannual changes in daily rainfall occurrence and 10-day dry spell frequencies at some individual stations. It is suggested that the NHMM provides a useful tool (a) to understand the statistics of daily rainfall occurrence at the station level in terms of large-scale atmospheric patterns, and (b) to produce station-scale daily rainfall sequence scenarios for input into crop models, etc.

scholarly journals Predictability of June–September Rainfall in Ethiopia

2007 ◽  
Vol 135 (2) ◽  
pp. 628-650 ◽  
Author(s):  
Diriba Korecha ◽  
Anthony G. Barnston
Keyword(s):  
Lead Time ◽  
Large Scale ◽  
Southern Oscillation ◽  
Summer Rainfall ◽  
Rainfall Anomaly ◽  
Seasonal Rainfall ◽  
Multivariate Statistical ◽  
Local Climate ◽  
Rainfall Prediction ◽  
Enso Predictability

Abstract In much of Ethiopia, similar to the Sahelian countries to its west, rainfall from June to September contributes the majority of the annual total, and is crucial to Ethiopia’s water resource and agriculture operations. Drought-related disasters could be mitigated by warnings if skillful summer rainfall predictions were possible with sufficient lead time. This study examines the predictive potential for June–September rainfall in Ethiopia using mainly statistical approaches. The skill of a dynamical approach to predicting the El Niño–Southern Oscillation (ENSO), which impacts Ethiopian rainfall, is assessed. The study attempts to identify global and more regional processes affecting the large-scale summer climate patterns that govern rainfall anomalies. Multivariate statistical techniques are applied to diagnose and predict seasonal rainfall patterns using historical monthly mean global sea surface temperatures and other physically relevant predictor data. Monthly rainfall data come from a newly assembled dense network of stations from the National Meteorological Agency of Ethiopia. Results show that Ethiopia’s June–September rainy season is governed primarily by ENSO, and secondarily reinforced by more local climate indicators near Africa and the Atlantic and Indian Oceans. Rainfall anomaly patterns can be predicted with some skill within a short lead time of the summer season, based on emerging ENSO developments. The ENSO predictability barrier in the Northern Hemisphere spring poses a major challenge to providing seasonal rainfall forecasts two or more months in advance. Prospects for future breakthroughs in ENSO prediction are thus critical to future improvements to Ethiopia’s summer rainfall prediction.

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