scholarly journals Assessment of spatial uncertainty of heavy rainfall at catchment scale using a dense gauge network

2019 ◽  
Vol 23 (7) ◽  
pp. 2863-2875 ◽  
Author(s):  
Sungmin O ◽  
Ulrich Foelsche
Keyword(s):  
Remote Sensing ◽  
Heavy Rainfall ◽  
Spatial Scales ◽  
Horizontal Resolution ◽  
Spatial Uncertainty ◽  
Catchment Scale ◽  
Rainfall Events ◽  
Areal Rainfall ◽  
The Impact ◽  
Heavy Rainfall Events

Abstract. Hydrology and remote-sensing communities have made use of dense rain-gauge networks for studying rainfall uncertainty and variability. However, in most regions, these dense networks are only available at small spatial scales (e.g., within remote-sensing subpixel areas) and over short periods of time. Just a few studies have applied a similar approach, i.e., employing dense gauge networks to catchment-scale areas, which limits the verification of their results in other regions. Using 10-year rainfall measurements from a network of 150 rain gauges, WegenerNet (WEGN), we assess the spatial uncertainty in observed heavy rainfall events. The WEGN network is located in southeastern Austria over an area of 20 km × 15 km with moderate orography. First, the spatial variability in rainfall in the region was characterized using a correlogram at daily and sub-daily scales. Differences in the spatial structure of rainfall events between warm and cold seasons are apparent, and we selected heavy rainfall events, the upper 10 % of wettest days during the warm season, for further analyses because of their high potential for causing hazards. Secondly, we investigated the uncertainty in estimating mean areal rainfall arising from a limited gauge density. The average number of gauges required to obtain areal rainfall with errors less than a certain threshold (≤20 % normalized root-mean-square error – RMSE – is considered here) tends to increase, roughly following a power law as the timescale decreases, while the errors can be significantly reduced by establishing regularly distributed gauges. Lastly, the impact of spatial aggregation on extreme rainfall was examined, using gridded rainfall data with various horizontal grid spacings. The spatial-scale dependence was clearly observed at high intensity thresholds and high temporal resolutions; e.g., the 5 min extreme intensity increases by 44 % for the 99.9th and by 25 % for the 99th percentile, with increasing horizontal resolution from 0.1 to 0.01∘. Quantitative uncertainty information from this study can guide both data users and producers to estimate uncertainty in their own observational datasets, consequently leading to the sensible use of the data in relevant applications. Our findings could be transferred to midlatitude regions with moderate topography, but only to a limited extent, given that regional factors that can affect rainfall type and process are not explicitly considered in the study.

scholarly journals Assessment of spatial uncertainty of heavy local rainfall using a dense gauge network

2018 ◽  
Author(s):  
Sungmin O ◽  
Ulrich Foelsche
Keyword(s):  
Heavy Rainfall ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Spatial Uncertainty ◽  
Wet Season ◽  
Rainfall Events ◽  
Rain Gauges ◽  
Areal Rainfall ◽  
The Impact ◽  
Heavy Rainfall Events

Abstract. Hydrology and remote-sensing communities have made use of dense rain-gauge networks for studying rainfall uncertainty and variability. However, in most regions, these dense networks are only available at sub-pixel scales and over short periods of time. Just a few studies have applied a similar approach, employing dense gauge networks, to local-scale areas, which limits the verification of their results in other regions. Using 10-year rainfall measurements from a network of 150 rain gauges, we assess spatial uncertainty in observed heavy rainfall events. The network is located in southeastern Austria over an area of 20 km × 15 km with no significant orography. First, the spatial variability of rainfall in the region was characterised using a correlogram at daily and sub-daily scales. Differences in the spatial structure of rainfall events between wet and dry seasons are apparent and we selected heavy rainfall events, the upper 10 % of wettest days during the wet season, for further analyses because of their high potential for causing hazard risk. Secondly, we investigated uncertainty in estimating mean areal rainfall arising from a limited gauge density. The number of gauges required to obtain areal rainfall with > 20 % accuracy tends to increase roughly following a power law as time scale decreases. Lastly, the impact of spatial aggregation on extreme rainfall was examined using gridded rainfall data with horizontal grid spacings from 0.1° to 0.01°. The spatial scale dependence was clearly observed at high intensity thresholds and high temporal resolutions. Quantitative uncertainty information from this study can guide both data users and producers to estimate uncertainty in their own observational datasets, consequently leading to the rational use of the data in relevant applications. Our findings are generalisable to other plain regions in mid-latitudes, however the degree of uncertainty could be affected by regional variations, like rainfall type or topography.

scholarly journals Impact of Radiance Data Assimilation on the Prediction of Heavy Rainfall in RMAPS: A Case Study

2018 ◽  
Vol 10 (9) ◽  
pp. 1380 ◽  
Author(s):  
Yanhui Xie ◽  
Jiancheng Shi ◽  
Shuiyong Fan ◽  
Min Chen ◽  
Youjun Dou ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Heavy Rainfall ◽  
Precipitation Forecast ◽  
Observation Data ◽  
Rainfall Events ◽  
Satellite Radiance Data ◽  
Radiance Data ◽  
The Impact ◽  
Heavy Rainfall Events

Herein, a case study on the impact of assimilating satellite radiance observation data into the rapid-refresh multi-scale analysis and prediction system (RMAPS) is presented. This case study targeted the 48 h period from 19–20 July 2016, which was characterized by the passage of a low pressure system that produced heavy rainfall over North China. Two experiments were performed and 24 h forecasts were produced every 3 h. The results indicated that the forecast prior to the satellite radiance data assimilation could not accurately predict heavy rainfall events over Beijing and the surrounding area. The assimilation of satellite radiance data from the advanced microwave sounding unit-A (AMSU-A) and microwave humidity sounding (MHS) improved the skills of the quantitative precipitation forecast to a certain extent. In comparison with the control experiment that only assimilated conventional observations, the experiment with the integrated satellite radiance data improved the rainfall forecast accuracy for 6 h accumulated precipitation after about 6 h, especially for rainfall amounts that were greater than 25 mm. The average rainfall score was improved by 14.2% for the 25 mm threshold and by 35.8% for 50 mm of rainfall. The results also indicated a positive impact of assimilating satellite radiances, which was primarily reflected by the improved performance of quantitative precipitation forecasting and higher spatial correlation in the forecast range of 6–12 h. Satellite radiance observations provided certain valuable information that was related to the temperature profile, which increased the scope of the prediction of heavy rainfall and led to an improvement in the rainfall scoring in the RMAPS. The inclusion of satellite radiance observations was found to have a small but beneficial impact on the prediction of heavy rainfall events as it relates to our case study conditions. These findings suggest that the assimilation of satellite radiance data in the RMAPS can provide an overall improvement in heavy rainfall forecasting.

scholarly journals Effects of Global Warming on Heavy Rainfall During the Baiu Season Projected by a Cloud-System-Resolving Model

2008 ◽  
Vol 3 (1) ◽  
pp. 15-24 ◽  
Author(s):  
Masaomi Nakamura ◽  
◽  
Sachie Kaneda ◽  
Yasutaka Wakazuki ◽  
Chiashi Muroi ◽  
...  
Keyword(s):  
Global Warming ◽  
Heavy Rainfall ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Extreme Rainfall ◽  
Horizontal Resolution ◽  
Rainfall Events ◽  
Future Global Warming ◽  
Heavy Rainfall Events

Under the Kyosei-4 Project, unprecedented high resolution global and regional climate models were developed on the Earth Simulator to investigate the effect of global warming on tropical cyclones, baiu frontal rainfall systems, and heavy rainfall events that could not be resolved using conventional climate models.For the regional climate model, a nonhydrostatic model (NHM) with a horizontal resolution of 5 km was developed to be used in the simulation of heavy rainfall during the baiu season in Japan. Simulations in June and July were executed for 10 years in present and future global warming climates. It was found that, due to global warming, mean rainfall is projected to increase except in eastern and northern Japan, the frequency of heavy rainfall events would increase and its increment rate become higher for heavier rainfall, and return values for extreme rainfall would grow.Experiments using an NHM with a horizontal resolution of 1 km were conducted to study the effects of resolution. Compared to 5 km resolution, it expresses the organization of rainfall systems causing heavy rainfall and the appearance-frequency distribution of rainfall for variable intensities more realistically.

scholarly journals Impact of Boreal Summer Intra-Seasonal Oscillations on the Heavy Rainfall Events in Taiwan during the 2017 Meiyu Season

Atmosphere ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 205 ◽  
Author(s):  
Wan-Ru Huang ◽  
Pin-Yi Liu ◽  
Jen-Her Chen ◽  
Liping Deng
Keyword(s):  
Heavy Rainfall ◽  
Boreal Summer ◽  
Economic Losses ◽  
Global Forecast System ◽  
Forecast System ◽  
Rainfall Events ◽  
The Impact ◽  
Meiyu Season ◽  
Heavy Rainfall Events ◽  
Seasonal Oscillations

During May and June (the Meiyu season) of 2017, Taiwan was affected by three heavy frontal rainfall events, which led to large economic losses. Using satellite observations and reanalysis data, this study investigates the impact of boreal summer intra-seasonal oscillations (BSISOs, including a 30–60 day ISO mode named BSISO1 and a 10–30 day ISO mode named BSISO2) on the heavy rainfall events in Taiwan during the 2017 Meiyu season. Our examinations show that BSISO2 is more important than BSISO1 in determining the formation of heavy rainfall events in Taiwan during the 2017 Meiyu season. The heavy rainfall events generally formed in Taiwan at phases 4–6 of BSISO2, when the enhanced southwesterly wind and moisture flux convergence center propagate northward into the Taiwan area. In addition, we examined the forecast rainfall data (at lead times of one day to 16 days) obtained from the National Centers for Environmental Prediction Global Forecast System (NCEPgfs) and the Taiwan Central Weather Bureau Global Forecast System (CWBgfs). Our results show that the better the model’s capability in forecasting the BSISO2 index is, the better the model’s capability in forecasting the timing of rainfall formation in Taiwan during the 2017 Meiyu season is. These findings highlight the importance of BSISO2 in affecting the rainfall characteristics in East Asia during the Meiyu season.

scholarly journals Verification of heavy rainfall in NWP models : A case study

MAUSAM ◽  
2021 ◽  
Vol 68 (4) ◽  
pp. 699-712
Author(s):  
KULDEEP SHARMA ◽  
RAGHAVENDRA ASHRIT ◽  
R. BHATLA ◽  
R. RAKHI ◽  
G. R. IYENGAR ◽  
...  
Keyword(s):  
Heavy Rainfall ◽  
Extreme Rainfall ◽  
Heavy Rain ◽  
Low Pressure ◽  
Modeling Framework ◽  
Deterministic Models ◽  
Rainfall Events ◽  
The Impact ◽  
Heavy Rainfall Events ◽  
Low Pressure Systems

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.

scholarly journals Verification of heavy rainfall warning over Bihar and Uttar Pradesh

MAUSAM ◽  
2021 ◽  
Vol 60 (2) ◽  
pp. 175-184
Author(s):  
M. MOHAPATRA ◽  
H. R. HATWAR ◽  
S. R. KALSI
Keyword(s):  
False Alarm ◽  
Heavy Rainfall ◽  
Spatial Scales ◽  
Uttar Pradesh ◽  
India Meteorological Department ◽  
Probability Of Detection ◽  
Rain Event ◽  
Rainfall Events ◽  
Synoptic Conditions ◽  
Heavy Rainfall Events

India Meteorological Department (IMD) issues heavy rainfall warning for a meteorological sub-division when the expected 24 hours rainfall over any rain gauge station in that sub-division is likely to be 64.5 mm or more. Though these warnings have been provided since long and are also now being issued for smaller spatial scales, very few attempts have been made for quantitative evaluation of these warnings.  Hence, a study is undertaken to verify the heavy rainfall warning over the representative meteorological sub-divisions of east Uttar Pradesh (UP), west UP and Bihar during main monsoon months of July and August. For this purpose data of the recent 5 years (2001-2005) and also for another epoch of 5 years in the early 1970s has been taken into consideration. In this connection, the day when heavy rainfall is recorded over atleast two stations in a sub-division, has been considered as a heavy rainfall day for that sub-division.   This study of verification shows that probability of detection of heavy rainfall is 64% over Bihar, 52% over east UP and 53% over west UP for the recent 5 years. Compared to early 1970s, there has been slight improvement in the forecast skill during 2001-2005 with probability of detection increasing by about 10-20% and with decrease in missing rate and false alarm rate. However, the false alarm rates are still large indicating higher bias towards over-prediction. The synoptic conditions associated with the heavy rainfall events have been collected for the period 2001-05 and analysed. The analysis of the unanticipated heavy rainfall events suggests that though proper interpretation of synoptic charts and NWP outputs could improve the warnings, the forecast system available even today is still not capable to capture every heavy rain event in advance.

scholarly journals Extreme Floods in the Eastern Part of Europe: Large-Scale Drivers and Associated Impacts

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1122
Author(s):  
Monica Ionita ◽  
Viorica Nagavciuc
Keyword(s):  
Water Vapor ◽  
Heavy Rainfall ◽  
Large Scale ◽  
Vapor Transport ◽  
Water Vapor Transport ◽  
Flood Events ◽  
Rainfall Events ◽  
Catchment Areas ◽  
Large Scale Atmospheric Circulation ◽  
Heavy Rainfall Events

The role of the large-scale atmospheric circulation in producing heavy rainfall events and floods in the eastern part of Europe, with a special focus on the Siret and Prut catchment areas (Romania), is analyzed in this study. Moreover, a detailed analysis of the socio-economic impacts of the most extreme flood events (e.g., July 2008, June–July 2010, and June 2020) is given. Analysis of the largest flood events indicates that the flood peaks have been preceded up to 6 days in advance by intrusions of high Potential Vorticity (PV) anomalies toward the southeastern part of Europe, persistent cut-off lows over the analyzed region, and increased water vapor transport over the catchment areas of Siret and Prut Rivers. The vertically integrated water vapor transport prior to the flood peak exceeds 300 kg m−1 s−1, leading to heavy rainfall events. We also show that the implementation of the Flood Management Plan in Romania had positive results during the 2020 flood event compared with the other flood events, when the authorities took several precaution measurements that mitigated in a better way the socio-economic impact and risks of the flood event. The results presented in this study offer new insights regarding the importance of large-scale atmospheric circulation and water vapor transport as drivers of extreme flooding in the eastern part of Europe and could lead to a better flood forecast and flood risk management.

scholarly journals Mesoscale Analysis of Heavy Rainfall Episodes from SoWMEX/TiMREX

2012 ◽  
Vol 69 (2) ◽  
pp. 521-537 ◽  
Author(s):  
Christopher A. Davis ◽  
Wen-Chau Lee
Keyword(s):  
Heavy Rainfall ◽  
Southwest Monsoon ◽  
Rainfall Events ◽  
Mesoscale Analysis ◽  
Mesoscale Structure ◽  
Profound Influence ◽  
Moist Condition ◽  
Virtual Temperature ◽  
Convection Initiation ◽  
Heavy Rainfall Events

Abstract The authors analyze the mesoscale structure accompanying two multiday periods of heavy rainfall during the Southwest Monsoon Experiment and the Terrain-Induced Mesoscale Rainfall Experiment conducted over and near Taiwan during May and June 2008. Each period is about 5–6 days long with episodic heavy rainfall events within. These events are shown to correspond primarily to periods when well-defined frontal boundaries are established near the coast. The boundaries are typically 1 km deep or less and feature contrasts of virtual temperature of only 2°–3°C. Yet, owing to the extremely moist condition of the upstream conditionally unstable air, these boundaries appear to exert a profound influence on convection initiation or intensification near the coast. Furthermore, the boundaries, once established, are long lived, possibly reinforced through cool downdrafts and prolonged by the absence of diurnal heating over land in generally cloudy conditions. These boundaries are linked phenomenologically with coastal fronts that occur at higher latitudes.

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