scholarly journals High-Resolution QPF Uncertainty and Its Implications for Flood Prediction: A Case Study for the Eastern Iowa Flood of 2016

2018 ◽  
Vol 19 (8) ◽  
pp. 1289-1304 ◽  
Author(s):  
Bong-Chul Seo ◽  
Felipe Quintero ◽  
Witold F. Krajewski
Keyword(s):  
High Resolution ◽  
Lead Time ◽  
Performance Metrics ◽  
Precipitation Forecast ◽  
Lead Times ◽  
Flood Prediction ◽  
Forecast Lead Time ◽  
Basin Scale ◽  
Hydrologic Prediction ◽  
Map Analysis

Abstract This study addresses the uncertainty of High-Resolution Rapid Refresh (HRRR) quantitative precipitation forecasts (QPFs), which were recently appended to the operational hydrologic forecasting framework. In this study, we examine the uncertainty features of HRRR QPFs for an Iowa flooding event that occurred in September 2016. Our evaluation of HRRR QPFs is based on the conventional approach of QPF verification and the analysis of mean areal precipitation (MAP) with respect to forecast lead time. The QPF verification results show that the precipitation forecast skill of HRRR significantly drops during short lead times and then gradually decreases for further lead times. The MAP analysis also demonstrates that the QPF error sharply increases during short lead times and starts decreasing slightly beyond 4-h lead time. We found that the variability of QPF error measured in terms of MAP decreases as basin scale and lead time become larger and longer, respectively. The effects of QPF uncertainty on hydrologic prediction are quantified through the hillslope-link model (HLM) simulations using hydrologic performance metrics (e.g., Kling–Gupta efficiency). The simulation results agree to some degree with those from the MAP analysis, finding that the performance achieved from the QPF forcing decreases during 1–3-h lead times and starts increasing with 4–6-h lead times. The best performance acquired at the 1-h lead time does not seem acceptable because of the large overestimation of the flood peak, along with an erroneous early peak that is not observed in streamflow observations. This study provides further evidence that HRRR contains a well-known weakness at short lead times, and the QPF uncertainty (e.g., bias) described as a function of forecast lead times should be corrected before its use in hydrologic prediction.

scholarly journals Limits to Flood Forecasting in the Colorado Front Range for Two Summer Convection Periods Using Radar Nowcasting and a Distributed Hydrologic Model

2013 ◽  
Vol 14 (4) ◽  
pp. 1075-1097 ◽  
Author(s):  
Hernan A. Moreno ◽  
Enrique R. Vivoni ◽  
David J. Gochis
Keyword(s):  
Lead Time ◽  
Flood Forecasting ◽  
Hydrologic Model ◽  
Colorado Front Range ◽  
Precipitation Forecast ◽  
Lead Times ◽  
Forecast Errors ◽  
Front Range ◽  
Distributed Hydrologic Model ◽  
Forecast Lead Time

Abstract Flood forecasting in mountain basins remains a challenge given the difficulty in accurately predicting rainfall and in representing hydrologic processes in complex terrain. This study identifies flood predictability patterns in mountain areas using quantitative precipitation forecasts for two summer events from radar nowcasting and a distributed hydrologic model. The authors focus on 11 mountain watersheds in the Colorado Front Range for two warm-season convective periods in 2004 and 2006. The effects of rainfall distribution, forecast lead time, and basin area on flood forecasting skill are quantified by means of regional verification of precipitation fields and analyses of the integrated and distributed basin responses. The authors postulate that rainfall and watershed characteristics are responsible for patterns that determine flood predictability at different catchment scales. Coupled simulations reveal that the largest decrease in precipitation forecast skill occurs between 15- and 45-min lead times that coincide with rapid development and movements of convective systems. Consistent with this, flood forecasting skill decreases with nowcasting lead time, but the functional relation depends on the interactions between watershed properties and rainfall characteristics. Across the majority of the basins, flood forecasting skill is reduced noticeably for nowcasting lead times greater than 30 min. The authors identified that intermediate basin areas [~(2–20) km2] exhibit the largest flood forecast errors with the largest differences across nowcasting ensemble members. The typical size of summer convective storms is found to coincide well with these maximum errors, while basin properties dictate the shape of the scale dependency of flood predictability for different lead times.

scholarly journals The Skill of ECMWF Medium-Range Forecasts during the Year of Tropical Convection 2008

2010 ◽  
Vol 138 (10) ◽  
pp. 3787-3805 ◽  
Author(s):  
Arindam Chakraborty
Keyword(s):  
Surface Temperature ◽  
Diurnal Cycle ◽  
Lead Time ◽  
Tropical Convection ◽  
Precipitation Forecast ◽  
Lead Times ◽  
Near Surface ◽  
Forecast Lead Time ◽  
Medium Range ◽  
Ecmwf Model

Abstract This study uses the European Centre for Medium-Range Weather Forecasts (ECMWF) model-generated high-resolution 10-day-long predictions for the Year of Tropical Convection (YOTC) 2008. Precipitation forecast skills of the model over the tropics are evaluated against the Tropical Rainfall Measuring Mission (TRMM) estimates. It has been shown that the model was able to capture the monthly to seasonal mean features of tropical convection reasonably. Northward propagation of convective bands over the Bay of Bengal was also forecasted realistically up to 5 days in advance, including the onset phase of the monsoon during the first half of June 2008. However, large errors exist in the daily datasets especially for longer lead times over smaller domains. For shorter lead times (less than 4–5 days), forecast errors are much smaller over the oceans than over land. Moreover, the rate of increase of errors with lead time is rapid over the oceans and is confined to the regions where observed precipitation shows large day-to-day variability. It has been shown that this rapid growth of errors over the oceans is related to the spatial pattern of near-surface air temperature. This is probably due to the one-way air–sea interaction in the atmosphere-only model used for forecasting. While the prescribed surface temperature over the oceans remain realistic at shorter lead times, the pattern and hence the gradient of the surface temperature is not altered with change in atmospheric parameters at longer lead times. It has also been shown that the ECMWF model had considerable difficulties in forecasting very low and very heavy intensity of precipitation over South Asia. The model has too few grids with “zero” precipitation and heavy (>40 mm day−1) precipitation. On the other hand, drizzle-like precipitation is too frequent in the model compared to that in the TRMM datasets. Further analysis shows that a major source of error in the ECMWF precipitation forecasts is the diurnal cycle over the South Asian monsoon region. The peak intensity of precipitation in the model forecasts over land (ocean) appear about 6 (9) h earlier than that in the observations. Moreover, the amplitude of the diurnal cycle is much higher in the model forecasts compared to that in the TRMM estimates. It has been seen that the phase error of the diurnal cycle increases with forecast lead time. The error in monthly mean 3-hourly precipitation forecasts is about 2–4 times of the error in the daily mean datasets. Thus, effort should be given to improve the phase and amplitude forecast of the diurnal cycle of precipitation from the model.

scholarly journals The effect of increased resolution of geostationary satellite imageries on predictability of tropical thunderstorms over Southeast Asia

2018 ◽  
Author(s):  
Kwonmin Lee ◽  
Hye-Sil Kim ◽  
Yong-Sang Choi
Keyword(s):  
High Resolution ◽  
Southeast Asia ◽  
Lead Time ◽  
Geostationary Satellite ◽  
Disaster Mitigation ◽  
Mature Stage ◽  
Lead Times ◽  
Low Resolution ◽  
High Resolution Imagery ◽  
Heavy Damage

Abstract. Tropical thunderstorms cause heavy damage to property and lives, and there is a strong interest in advancing the predictability of thunderstorms with more precise satellite observations. Using high-resolution (2 km and 10 minutes) imageries from the geostationary satellite (Himawari-8) recently launched over Southeast Asia, we examine how early the thunderstorms can be predicted compared to the low-resolution (4 km and 30 minutes) imageries of the former satellite. We compare the lead times for eight thunderstorms that occurred in August 2017 between high- and low-resolution imageries. These thunderstorms are identified by pixels with a brightness temperature at 10.45 μm (BT11) gradually decreasing by more than 5 K per 10 minutes (15 K per 30 minutes) compared to the previous imagery. The lead time is then calculated as the time passed from the initial to the mature stage of the thunderstorm signal, based on the time series of a minimum BT11 of these pixels. The lead time is found to be 100–180 minutes for the high-resolution imagery, while it is only found to be 30 minutes if detectable at all for the low-resolution imagery. This result suggests that the high-resolution imagery is essential for substantial disaster mitigation because of its ability to note an alarm more than two hours ahead of a matured thunderstorm.

Probabilistic precipitation nowcast for flash flooding purposes across Australia: verification of a new version of Short-Term Ensemble Prediction System (STEPS)

2021 ◽  
Author(s):  
Carlos Velasco-Forero ◽  
Jayaram Pudashine ◽  
Mark Curtis ◽  
Alan Seed
Keyword(s):  
Lead Time ◽  
Precipitation Forecast ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Prediction System ◽  
Short Term ◽  
Radar Rainfall ◽  
Flash Flooding ◽  
Ensemble Prediction System ◽  
Study Results

<div> <p>Short-term precipitation forecast plays a vital role for minimizing the adverse effects of heavy precipitation events such as flash flooding.  Radar rainfall nowcasting techniques based on statistical extrapolations are used to overcome current limitations of precipitation forecasts from numerical weather models, as they provide high spatial and temporal resolutions forecasts within minutes of the observation time. Among various algorithms, the Short-Term Ensemble Prediction System (STEPS) provides rainfall fields nowcasts in a probabilistic sense by accounting the uncertainty in the precipitation forecasts by means of ensembles, with spatial and temporal characteristic very similar to those in the observed radar rainfall fields. The Australian Bureau of Meteorology uses STEPS to generate ensembles of forecast rainfall ensembles in real-time from its extensive weather radar network. </p> </div><div> <p>In this study, results of a large probabilistic verification exercise to a new version of STEPS (hereafter named STEPS-3) are reported. An extensive dataset of more than 47000 individual 5-minute radar rainfall fields (the equivalent of more than 163 days of rain) from ten weather radars across Australia (covering tropical to mid-latitude regions) were used to generate (and verify) 96-member rainfall ensembles nowcasts with up to a 90-minute lead time. STEPS-3 was found to be more than 15-times faster in delivering results compared with previous version of STEPS and an open-source algorithm called pySTEPS. Interestingly, significant variations were observed in the quality of predictions and verification results from one radar to other, from one event to other, depending on the characteristics and location of the radar, nature of the rainfall event, accumulation threshold and lead time. For example, CRPS and RMSE of ensembles of 5-min rainfall forecasts for radars located in mid-latitude regions are better (lower) than those ones from radars located in tropical areas for all lead-times. Also, rainfall fields from S-band radars seem to produce rainfall forecasts able to successfully identify extreme rainfall events for lead times up to 10 minutes longer than those produced using C-band radar datasets for the same rain rate thresholds. Some details of the new STEPS-3 version, case studies and examples of the verification results will be presented. </p> </div>

scholarly journals Effects of Increasing Horizontal Resolution in a Convection-Permitting Model on Flood Forecasting: The 2011 Dramatic Events in Liguria, Italy

2015 ◽  
Vol 16 (4) ◽  
pp. 1843-1856 ◽  
Author(s):  
Silvio Davolio ◽  
Francesco Silvestro ◽  
Piero Malguzzi
Keyword(s):  
High Resolution ◽  
Weather Prediction ◽  
Flood Forecasting ◽  
Horizontal Resolution ◽  
Precipitation Forecast ◽  
Limited Area ◽  
Flood Prediction ◽  
Meteorological Model ◽  
The Impact ◽  
Atmospheric Simulations

Abstract Coupling meteorological and hydrological models is a common and standard practice in the field of flood forecasting. In this study, a numerical weather prediction (NWP) chain based on the BOLogna Limited Area Model (BOLAM) and the MOdello LOCale in Hybrid coordinates (MOLOCH) was coupled with the operational hydrological forecasting chain of the Ligurian Hydro-Meteorological Functional Centre to simulate two major floods that occurred during autumn 2011 in northern Italy. Different atmospheric simulations were performed by varying the grid spacing (between 1.0 and 3.0 km) of the high-resolution meteorological model and the set of initial/boundary conditions driving the NWP chain. The aim was to investigate the impact of these parameters not only from a meteorological perspective, but also in terms of discharge predictions for the two flood events. The operational flood forecasting system was thus used as a tool to validate in a more pragmatic sense the quantitative precipitation forecast obtained from different configurations of the NWP system. The results showed an improvement in flood prediction when a high-resolution grid was employed for atmospheric simulations. In turn, a better description of the evolution of the precipitating convective systems was beneficial for the hydrological prediction. Although the simulations underestimated the severity of both floods, the higher-resolution model chain would have provided useful information to the decision-makers in charge of protecting citizens.

scholarly journals Effects of high-resolution geostationary satellite imagery on the predictability of tropical thunderstorms over Southeast Asia

2019 ◽  
Vol 19 (10) ◽  
pp. 2241-2248
Author(s):  
Kwonmin Lee ◽  
Hye-Sil Kim ◽  
Yong-Sang Choi
Keyword(s):  
High Resolution ◽  
Southeast Asia ◽  
Lead Time ◽  
Geostationary Satellite ◽  
Disaster Mitigation ◽  
Lead Times ◽  
Low Resolution ◽  
Initial State ◽  
High Resolution Imagery ◽  
Significant Damage

Abstract. Tropical thunderstorms cause significant damage to property and lives, and a strong research interest exists in the advances and improvement of thunderstorm predictability by satellite observations. Using high-resolution (2 km and 10 min) imagery from the geostationary satellite, Himawari-8, recently launched over Southeast Asia, we examined the earliest possible time for the prediction of thunderstorms as compared to the potential of low-resolution (4 km and 30 min) imagery of the former satellite. We compared the lead times of high- and low-resolution imageries of 60 tropical thunderstorms that occurred in August 2017. These thunderstorms were identified by the decreasing trend in the 10.45 µm brightness temperature (BT11) by over 5 K per 10 min for the high-resolution imagery and 15 K per 30 min for the low-resolution imagery. The lead time was then calculated over the time from the initial state to the mature state of the thunderstorm, based on the time series of a minimum BT11 of thunderstorm pixels. The lead time was found to be 90–180 min for the high-resolution imagery, whereas it was only 60 min (if detectable) for the low-resolution imagery. These results indicate that high-resolution imagery is essential for substantial disaster mitigation owing to its ability to raise an alarm more than 2 h ahead of the mature state of a tropical thunderstorm.

Uncertainty Evaluation in a Flood Forecasting Model Using JMA Numerical Weather Prediction

2009 ◽  
Vol 4 (4) ◽  
pp. 600-605 ◽  
Author(s):  
Hadi Kardhana ◽  
◽  
Akira Mano ◽  
Keyword(s):  
Numerical Weather Prediction ◽  
Lead Time ◽  
Weather Prediction ◽  
Japan Meteorological Agency ◽  
Rainfall Runoff ◽  
Flood Prediction ◽  
Numerical Weather ◽  
Forecast Lead Time ◽  
Rainfall Runoff Model ◽  
Runoff Model

Numerical weather prediction (NWP) is useful in flood prediction using a rainfall-runoff model. Uncertainty occurring in the forecast, however, adversely affects flood prediction accuracy, in addition to uncertainty inherent in the rainfall-runoff model. Clarifying this uncertainty and its magnitude is expected to lead to wider forecast applications. Taking the case of Japan’s Shichikashuku Dam, 6 flood events between 2002 and 2007 were analyzed. NWP was based on short-range forecasts by the Japan Meteorological Agency (JMA). The rainfall-runoff model is based on a distributed tank model. This research calculates uncertainty by identifying and quantifying the relative error of forecasts by a) NWP and b) the runoff model. Results showed that NAP is the main cause of flood forecast uncertainty. They also showed the correlation between forecast lead time and uncertainty. Uncertainty rises with longer lead time, corresponding to the magnitude of observed discharge and precipitation.

scholarly journals Assessing the predictability of WRF model for heavy rain by cold air associated with the easterly wind at high-level patterns over mid-central Vietnam

2018 ◽  
Vol 34 (1S) ◽  
Author(s):  
Nguyen Tien Toan ◽  
Cong Thanh ◽  
Pham Thi Phuong ◽  
Vu Tuan Anh
Keyword(s):  
Lead Time ◽  
Wrf Model ◽  
Heavy Rain ◽  
Rainfall Threshold ◽  
Precipitation Forecast ◽  
Easterly Wind ◽  
Cold Air ◽  
Central Vietnam ◽  
Forecast Lead Time ◽  
High Level

Abstract: In this study, the authors assessed the possibility of predicting precipitation due to cold air associated with the easterly wind at the high level in the WRF model with 2 days lead time for the Mid-Central Vietnam region. The results show that in the 24-hour forecasts lead time should use medium rainfall threshold (16-50 mm/day) and heavy rain (50-100 mm/day) to referent for quantitative precipitation forecast and rainfall area; for 48-hour forecast lead time should choose moderate rainfall threshold. The threshold of over 100 mm, the results of all of forecast lead time is not good, almost unpredictable. The results of this study can help forecasters have more information for forecasting rain due to cold air associated easterly wind at the high level for the Mid-Central Vietnam region.

scholarly journals A Review of Quantitative Precipitation Forecasts and Their Use in Short- to Medium-Range Streamflow Forecasting

2011 ◽  
Vol 12 (5) ◽  
pp. 713-728 ◽  
Author(s):  
Lan Cuo ◽  
Thomas C. Pagano ◽  
Q. J. Wang
Keyword(s):  
Lead Time ◽  
Weather Prediction ◽  
Research Field ◽  
Lead Times ◽  
Streamflow Forecasting ◽  
Streamflow Prediction ◽  
Forecast Lead Time ◽  
Quantitative Precipitation Forecasts ◽  
Active Research ◽  
Nwp Model

Abstract Unknown future precipitation is the dominant source of uncertainty for many streamflow forecasts. Numerical weather prediction (NWP) models can be used to generate quantitative precipitation forecasts (QPF) to reduce this uncertainty. The usability and usefulness of NWP model outputs depend on the application time and space scales as well as forecast lead time. For streamflow nowcasting (very short lead times; e.g., 12 h), many applications are based on measured in situ or radar-based real-time precipitation and/or the extrapolation of recent precipitation patterns. QPF based on NWP model output may be more useful in extending forecast lead time, particularly in the range of a few days to a week, although low NWP model skill remains a major obstacle. Ensemble outputs from NWP models are used to articulate QPF uncertainty, improve forecast skill, and extend forecast lead times. Hydrologic prediction driven by these ensembles has been an active research field, although operational adoption has lagged behind. Conversely, relatively little study has been done on the hydrologic component (i.e., model, parameter, and initial condition) of uncertainty in the streamflow prediction system. Four domains of research are identified: selection and evaluation of NWP model–based QPF products, improved QPF products, appropriate hydrologic modeling, and integrated applications.

scholarly journals Eastern U.S. Verification of Ensemble Precipitation Forecasts

2017 ◽  
Vol 32 (1) ◽  
pp. 117-139 ◽  
Author(s):  
Sanjib Sharma ◽  
Ridwan Siddique ◽  
Nicholas Balderas ◽  
Jose D. Fuentes ◽  
Seann Reed ◽  
...  
Keyword(s):  
United States ◽  
Lead Time ◽  
Weather Prediction ◽  
The United States ◽  
Ensemble Forecast ◽  
Spatial Aggregation ◽  
Precipitation Forecast ◽  
Lead Times ◽  
Eastern United States ◽  
Skill Scores

Abstract The quality of ensemble precipitation forecasts across the eastern United States is investigated, specifically, version 2 of the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System Reforecast (GEFSRv2) and Short Range Ensemble Forecast (SREF) system, as well as NCEP’s Weather Prediction Center probabilistic quantitative precipitation forecast (WPC-PQPF) guidance. The forecasts are verified using multisensor precipitation estimates and various metrics conditioned upon seasonality, precipitation threshold, lead time, and spatial aggregation scale. The forecasts are verified, over the geographic domain of each of the four eastern River Forecasts Centers (RFCs) in the United States, by considering first 1) the three systems or guidance, using a common period of analysis (2012–13) for lead times from 1 to 3 days, and then 2) GEFSRv2 alone, using a longer period (2004–13) and lead times from 1 to 16 days. The verification results indicate that, across the eastern United States, precipitation forecast bias decreases and the skill and reliability improve as the spatial aggregation scale increases; however, all the forecasts exhibit some underforecasting bias. The skill of the forecasts is appreciably better in the cool season than in the warm one. The WPC-PQPFs tend to be superior, in terms of the correlation coefficient, relative mean error, reliability, and forecast skill scores, than both GEFSRv2 and SREF, but the performance varies with the RFC and lead time. Based on GEFSRv2, medium-range precipitation forecasts tend to have skill up to approximately day 7 relative to sampled climatology.

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