scholarly journals Reconstruction of sub-daily rainfall sequences using multinomial multiplicative cascades

2010 ◽  
Vol 7 (4) ◽  
pp. 5267-5297 ◽  
Author(s):  
L. Wang ◽  
C. Onof ◽  
C. Maksimovic
Keyword(s):  
Daily Rainfall ◽  
Cascade Process ◽  
Short Term ◽  
Multiplicative Cascades ◽  
Hourly Rainfall ◽  
Cascade Method ◽  
Multiplicative Cascade

Abstract. This work aims to develop a semi-deterministic multiplicative cascade method for producing reliable short-term (sub-daily) rainfall sequences. The scaling feature of sub-daily rainfall sequences is analysed over the timescales of interest (i.e., 5 min to hourly in this research) to help derive the crucial parameters, i.e., the fragmentation ratios, for the proposed method. These derived ratios are then further used to stochastically disaggregate hourly rainfall sequences to 5-min using the multiplicative cascade process. The log-Poisson distributed cascade method is involved in this work to validate the proposed methodology by comparing certain statistics of the generated rainfall sequences over a specific range of timescales. The results demonstrate that the proposed methodology in general has the ability to reproduce the patterns of sub-daily rainfall observations from Greenwich raingauge station in London.

scholarly journals TEMPORAL RAINFALL DISAGGREGATION BY A SIMPLE RANDOM CASCADE MODEL

2018 ◽  
Vol 80 (6) ◽  
Author(s):  
Siti Mariam Saad ◽  
Abdul Aziz Jemain ◽  
Noriszura Ismail
Keyword(s):  
Rainfall Variability ◽  
Daily Rainfall ◽  
Poisson Model ◽  
Rain Gauge ◽  
Peninsular Malaysia ◽  
Cascade Model ◽  
Statistical Measures ◽  
Hourly Rainfall ◽  
Cascade Models ◽  
Log Normal

This study evaluates the utility and suitability of a simple discrete multiplicative random cascade model for temporal rainfall disaggregation. Two of a simple random cascade model, namely log-Poisson and log-Normal  models are applied to simulate hourly rainfall from daily rainfall at seven rain gauge stations in Peninsular Malaysia. The cascade models are evaluated based on the capability to simulate data that preserve three important properties of observed rainfall: rainfall variability, intermittency and extreme events. The results show that both cascade models are able to simulate reasonably well the commonly used statistical measures for rainfall variability (e.g. mean and standard deviation) of hourly rainfall. With respect to rainfall intermittency, even though both models are underestimated, the observed dry proportion, log-Normal  model is likely to simulate number of dry spells better than log-Poisson model. In terms of rainfall extremes, it is demonstrated that log-Poisson and log-Normal  models gave a satisfactory performance for most of the studied stations herein, except for Dungun and Kuala Krai stations, which both located in the east part of Peninsula.

scholarly journals A Comparison Between Global Satellite Mapping of Precipitation Data and High-Resolution Radar Data – A Case Study of Localized Torrential Rainfall over Japan

2021 ◽  
Vol 16 (4) ◽  
pp. 786-793
Author(s):  
Yoshiaki Hayashi ◽  
Taichi Tebakari ◽  
Akihiro Hashimoto ◽  
◽  
Keyword(s):  
High Resolution ◽  
Heavy Rainfall ◽  
Daily Rainfall ◽  
Radar Data ◽  
Rain Gauge ◽  
Hourly Rainfall ◽  
The Difference ◽  
Microwave Imager ◽  
Satellite Mapping

This paper presents a case study comparing the latest algorithm version of Global Satellite Mapping of Precipitation (GSMaP) data with C-band and X-band Multi-Parameter (MP) radar as high-resolution rainfall data in terms of localized heavy rainfall events. The study also obliged us to clarify the spatial and temporal resolution of GSMaP data using high-accuracy ground-based radar, and evaluate the performance and reporting frequency of GSMaP satellites. The GSMaP_Gauge_RNL data with less than 70 mm/day of daily rainfall was similar to the data of both radars, but the GSMaP_Gauge_RNL data with over 70 mm/day of daily rainfall was not, and the calibration by rain-gauge data was poor. Furthermore, both direct/indirect observations by the Global Precipitation Measurement/Microwave Imager (GPM/GMI) and the frequency thereof (once or twice) significantly affected the difference between GPM/GMI data and C-band radar data when the daily rainfall was less than 70 mm/day and the hourly rainfall was less than 20 mm/h. Therefore, it is difficult for GSMaP_Gauge to accurately estimate localized heavy rainfall with high-density particle precipitation.

scholarly journals Nine-Year Systematic Evaluation of the GPM and TRMM Precipitation Products in the Shuaishui River Basin in East-Central China

2020 ◽  
Vol 12 (6) ◽  
pp. 1042 ◽  
Author(s):  
Xiaoying Yang ◽  
Yang Lu ◽  
Mou Leong Tan ◽  
Xiaogang Li ◽  
Guoqing Wang ◽  
...  
Keyword(s):  
River Basin ◽  
Time Scales ◽  
Daily Rainfall ◽  
Central China ◽  
Spatiotemporal Resolution ◽  
Study Region ◽  
East Central ◽  
Hourly Rainfall ◽  
Rainfall Estimates ◽  
Different Time Scales

Owing to their advantages of wide coverage and high spatiotemporal resolution, satellite precipitation products (SPPs) have been increasingly used as surrogates for traditional ground observations. In this study, we have evaluated the accuracy of the latest five GPM IMERG V6 and TRMM 3B42 V7 precipitation products across the monthly, daily, and hourly scale in the hilly Shuaishui River Basin in East-Central China. For evaluation, a total of four continuous and three categorical metrics have been calculated based on SPP estimates and historical rainfall records at 13 stations over a period of 9 years from 2009 to 2017. One-way analysis of variance (ANOVA) and multiple posterior comparison tests are used to assess the significance of the difference in SPP rainfall estimates. Our evaluation results have revealed a wide-ranging performance among the SPPs in estimating rainfall at different time scales. Firstly, two post-time SPPs (IMERG_F and 3B42) perform considerably better in estimating monthly rainfall. Secondly, with IMERG_F performing the best, the GPM products generally produce better daily rainfall estimates than the TRMM products. Thirdly, with their correlation coefficients all falling below 0.6, neither GPM nor TRMM products could estimate hourly rainfall satisfactorily. In addition, topography tends to impose similar impact on the performance of SPPs across different time scales, with more estimation deviations at high altitude. In general, the post-time IMERG_F product may be considered as a reliable data source of monthly or daily rainfall in the study region. Effective bias-correction algorithms incorporating ground rainfall observations, however, are needed to further improve the hourly rainfall estimates of the SPPs to ensure the validity of their usage in real-world applications.

scholarly journals Deriving intensity–duration–frequency (IDF) curves using downscaled in situ rainfall assimilated with remote sensing data

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Yabin Sun ◽  
Dadiyorto Wendi ◽  
Dong Eon Kim ◽  
Shie-Yui Liong
Keyword(s):  
Remote Sensing ◽  
Daily Rainfall ◽  
Remote Sensing Data ◽  
Rainfall Events ◽  
Hourly Rainfall ◽  
Idf Curves ◽  
Intensity Duration Frequency ◽  
Improved Accuracy

AbstractThe rainfall intensity–duration–frequency (IDF) curves play an important role in water resources engineering and management. The applications of IDF curves range from assessing rainfall events, classifying climatic regimes, to deriving design storms and assisting in designing urban drainage systems, etc. The deriving procedure of IDF curves, however, requires long-term historical rainfall observations, whereas lack of fine-timescale rainfall records (e.g. sub-daily) often results in less reliable IDF curves. This paper presents the utilization of remote sensing sub-daily rainfall, i.e. Global Satellite Mapping of Precipitation (GSMaP), integrated with the Bartlett-Lewis rectangular pulses (BLRP) model, to disaggregate the daily in situ rainfall, which is then further used to derive more reliable IDF curves. Application of the proposed method in Singapore indicates that the disaggregated hourly rainfall, preserving both the hourly and daily statistic characteristics, produces IDF curves with significantly improved accuracy; on average over 70% of RMSE is reduced as compared to the IDF curves derived from daily rainfall observations.

scholarly journals Improved input to distributed hydrologic model in areas with sparse subdaily rainfall data using multivariate daily rainfall disaggregation

2018 ◽  
Vol 20 (4) ◽  
pp. 784-797 ◽  
Author(s):  
Marija Ivković ◽  
Andrijana Todorović ◽  
Jasna Plavšić
Keyword(s):  
Daily Rainfall ◽  
Flood Forecasting ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Rainfall Data ◽  
Rain Gauges ◽  
Observation Network ◽  
Hourly Rainfall ◽  
Extreme Flood ◽  
Flood Simulations

Abstract Flood forecasting relies on good quality of observed and forecasted rainfall. In Serbia, the recording rain gauge network is sparse and rainfall data mainly come from dense non-recording rain gauges. This is not beneficial for flood forecasting in smaller catchments and short-duration events, when hydrologic models operating on subdaily scale are applied. Moreover, differences in rainfall amounts from two types of gauges can be considerable, which is common in operational hydrological practice. This paper examines the possibility of including daily rainfall data from dense observation networks in flood forecasting based on subdaily data, using the extreme flood event in the Kolubara catchment in May 2014 as a case study. Daily rainfall from a dense observation network is disaggregated to hourly scale using the MuDRain multivariate disaggregation software. The disaggregation procedure results in well-reproduced rainfall dynamics and adjusts rainfall volume to the values from the non-recording gauges. The fully distributed wflow_hbv model, which is under development as a forecasting tool for the Kolubara catchment, is used for flood simulations with two alternative hourly rainfall data. The results show an improvement when the disaggregated rainfall from denser network is used, thus indicating the significance of better representation of rainfall temporal and spatial variability for flood forecasting.

scholarly journals A Nonparametric Stochastic Approach for Disaggregation of Daily to Hourly Rainfall Using 3-Day Rainfall Patterns

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2306
Author(s):  
Heeseong Park ◽  
Gunhui Chung
Keyword(s):  
Time Series Data ◽  
Daily Rainfall ◽  
Rainfall Event ◽  
Rainfall Data ◽  
Series Data ◽  
K Nearest Neighbor ◽  
Urban Catchment ◽  
Urban Flood ◽  
Hourly Rainfall ◽  
Rainfall Patterns

As infrastructure and populations are highly condensed in megacities, urban flood management has become a significant issue because of the potentially severe loss of lives and properties. In the megacities, rainfall from the catchment must be discharged throughout the stormwater pipe networks of which the travel time is less than one hour because of the high impervious rate. For a more accurate calculation of runoff from the urban catchment, hourly or even sub-hourly (minute) rainfall data must be applied. However, the available data often fail to meet the hydrologic system requirements. Many studies have been conducted to disaggregate time-series data while preserving distributional statistics from observed data. The K-nearest neighbor resampling (KNNR) method is a useful application of the nonparametric disaggregation technique. However, it is not easy to apply in the disaggregation of daily rainfall data into hourly while preserving statistical properties and boundary continuity. Therefore, in this study, three-day rainfall patterns were proposed to improve reproducible ability of statistics. Disaggregated rainfall was resampled only from a group having the same three-day rainfall patterns. To show the applicability of the proposed disaggregation method, probability distribution and L-moment statistics were compared. The proposed KNNR method with three-day rainfall patterns reproduced better the characteristics of rainfall event such as event duration, inter-event time, and toral amount of rainfall event. To calculate runoff from urban catchment, rainfall event is more important than hourly rainfall depth itself. Therefore, the proposed stochastic disaggregation method is useful to hydrologic analysis, particularly in rainfall disaggregation.

scholarly journals Temporal Downscaling of TRMM Rain-Rate Images Using Principal Component Analysis during Heavy Tropical Thunderstorm Seasons

2015 ◽  
Vol 16 (5) ◽  
pp. 2264-2275 ◽  
Author(s):  
M. Rizaludin Mahmud ◽  
Hiroshi Matsuyama ◽  
Tetsuro Hosaka ◽  
Shinya Numata ◽  
Mazlan Hashim
Keyword(s):  
Principal Component Analysis ◽  
Satellite Data ◽  
Rain Rate ◽  
Daily Rainfall ◽  
Principal Component ◽  
Component Analysis ◽  
Eastern Coast ◽  
Rain Gauges ◽  
Hourly Rainfall ◽  
Rainfall Estimates

Abstract This paper examines the utility of principal component analysis (PCA) in obtaining accurate daily rainfall estimates from 3-hourly Tropical Rainfall Measuring Mission (TRMM) satellite data during heavy precipitation in a humid tropical environment. A large bias during heavy thunderstorms in humid tropical catchments is indicated by the TRMM satellite and is of profound concern because it is a conspicuous constraint for practical hydrology applications and requires proper treatment, particularly in areas with sparse rain gauges. The common procedure of calculating daily rainfall estimates by direct accumulation (DA) of a series of 3-hourly rainfall estimates caused a large bias because of temporal uncertainties, upscaling effects, and different mechanisms. In this study, PCA was used to transform correlated 3-hourly rain-rate images into a minimum effective principal component and to compute the corresponding rain-rate proportion based on correlation strength. This study was conducted on 91 rainy days of various intensity, acquired from three different years, during the wettest season on the eastern coast of peninsular Malaysia. Results showed that PCA reduced the bias and daily root-mean-square error by an average of 62% and 22%, respectively, compared with the DA approach. The PCA transformation was able to produce more precise daily rainfall estimates compared to the DA approach without the use of any rain gauge references. However, the performance was varied by the threshold selection and rainfall intensity. The results of this study indicate that PCA can be a useful tool in effective temporal downscaling of TRMM satellite data during heavy thunderstorm seasons in areas where rain gauges are sparse and satellite data are pivotal as a secondary source of rainfall data.

scholarly journals Relationship between Surface Temperature and Extreme Rainfalls: A Multi-Time-Scale and Event-Based Analysis*

2014 ◽  
Vol 15 (5) ◽  
pp. 1999-2011 ◽  
Author(s):  
Gérémy Panthou ◽  
Alain Mailhot ◽  
Edward Laurence ◽  
Guillaume Talbot
Keyword(s):  
Relative Humidity ◽  
Rainfall Intensity ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Limiting Factor ◽  
Coastal Regions ◽  
Dewpoint Temperature ◽  
Hourly Rainfall ◽  
Event Based ◽  
The Relationship

Abstract Recent studies have examined the relationship between the intensity of extreme rainfall and temperature. Two main reasons justify this interest. First, the moisture-holding capacity of the atmosphere is governed by the Clausius–Clapeyron (CC) equation. Second, the temperature dependence of extreme-intensity rainfalls should follow a similar relationship assuming relative humidity remains constant and extreme rainfalls are driven by the actual water content of the atmosphere. The relationship between extreme rainfall intensity and air temperature (Pextr–Ta) was assessed by analyzing maximum daily rainfall intensities for durations ranging from 5 min to 12 h for more than 100 meteorological stations across Canada. Different factors that could influence this relationship have been analyzed. It appears that the duration and the climatic region have a strong influence on this relationship. For short durations, the Pextr–Ta relationship is close to the CC scaling for coastal regions while a super-CC scaling followed by an upper limit is observed for inland regions. As the duration increases, the slope of the relationship Pextr–Ta decreases for all regions. The shape of the Pextr–Ta curve is not sensitive to the percentile or season. Complementary analyses have been carried out to understand the departures from the expected Clausius–Clapeyron scaling. The relationship between dewpoint temperature and extreme rainfall intensity shows that the relative humidity is a limiting factor for inland regions, but not for coastal regions. Using hourly rainfall series, an event-based analysis is proposed in order to understand other deviations (super-CC, sub-CC, and monotonic decrease). The analyses suggest that the observed scaling is primarily due to the rainfall event dynamic.

scholarly journals Observations of Extreme Short-Term Precipitation Associated with Supercells and Mesovortices

2019 ◽  
Vol 148 (1) ◽  
pp. 159-182 ◽  
Author(s):  
Erik R. Nielsen ◽  
Russ S. Schumacher
Keyword(s):  
Flash Flood ◽  
Stage Iv ◽  
The United States ◽  
Surface Boundary ◽  
Short Term ◽  
Central Plains ◽  
Hourly Rainfall ◽  
High Precipitation ◽  
Rain Rates ◽  
Term Rain

Abstract Extreme hourly rainfall accumulations (e.g., exceeding 75 mm h−1) in several noteworthy flash flood events have suggested that the most intense accumulations were attendant with discrete mesoscale rotation or rotation embedded within larger organized systems. This research aims to explore how often extreme short-term rain rates in the United States are associated with storm-scale or mesoscale vortices. Five years of METAR observations and three years of Stage-IV analyses were obtained and filtered for hourly accumulations over 75 and 100 mm, respectively, clustered into events, and subjectively identified for rotation. The distribution of the short-term, locally extreme events shows the majority of the events were located along the Atlantic and Gulf of Mexico coastlines with additional events occurring in the central plains and into the Midwest. Nearly 50% of the cases were associated with low-level rotation in high-precipitation supercells or mesoscale vortices embedded in organized storm modes. Rotation events occurred more clearly in the warm sector, while nonrotation events tended to occur along a surface boundary. The rotation events tended to produce higher hourly accumulations over a larger region, but were associated with somewhat stronger synoptic-to-mesoscale forcing for ascent and more total column moisture. These results support recent modeling results suggesting that rotationally induced dynamic vertical pressure perturbations should not be ignored when it comes to extreme precipitation and can potentially enhance the short-term rain rates.

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