Assessment of GloFAS ensemble flood forecast for the Brahmaputra basin: skilful lead-times to predict monsoon floods for early action in Bangladesh

2021 ◽  
Author(s):  
Sazzad Hossain ◽  
Hannah Cloke ◽  
Andrea Ficchì ◽  
Christel Prudhomme ◽  
Arifuzzaman Bhuyan ◽  
...  
Keyword(s):  
Lead Time ◽  
Probability Of Detection ◽  
Forecast Skill ◽  
Lead Times ◽  
Management Service ◽  
Flood Forecast ◽  
Flood Events ◽  
South Asian Summer Monsoon ◽  
Discharge Data ◽  
Brahmaputra Basin

<p>Flood is a frequent natural hazard in the Brahmaputra basin in Bangladesh during the South Asian summer monsoon between June to September. When will flooding start during monsoon and how long it will last are two important questions that forecasters need to answer. Predicting flood timing and duration with a sufficient lead-time is challenging for forecasters due to strong intraseasonal variation of floods within a monsoon.</p><p>The GloFAS forecasting system is run by ECMWF as part of the Copernicus Emergency Management Service and provides operational extended-range ensemble flood forecast with 30 days lead-time for the major river basins in the world. In this study, we evaluated GloFAS reforecasts for the Brahmaputra basin in Bangladesh for the period 1997–2019 at different lead-times against observed stream gauge and ECMWF ERA5 reanalysis river discharge data. We used various probabilistic forecast verification metrics, such as Relative Operating Characteristic (ROC), False Alarm Ratio (FAR), and Probability of Detection (POD), to study how forecast skill varies over different lead-times. We also assessed the skilful lead-times of the GloFAS forecast to predict flood timing and duration during the monsoon. These scores were calculated considering relevant flood threshold levels and action-based parameters, such as Action Lifetime, based on user needs in Bangladesh. The GloFAS forecast case study for the recent 2020 monsoon floods in the Brahmaputra basin shows that the onset of flood events was successfully predicted with a lead-time of 15 days. These forecasts were disseminated among the different stakeholders, including humanitarian agencies, flood and disaster management organisations, to inform forecast-based actions, such as evacuation of vulnerable people to safer places ahead of flood events. Our study demonstrates that GloFAS ability to predict monsoon floods in terms of timing and duration can improve national flood forecasting capabilities providing sufficient lead-time for early actions in Bangladesh. The study will help forecasters as well as users to understand forecast skill and associated uncertainty in probabilistic forecasts to predict flood events in Bangladesh.</p><p> </p><p> </p><p> </p>

Exploring the links between hydrological forecast skill and multiple flood hazard drivers in southern Africa

2020 ◽  
Author(s):  
Andrea Ficchi ◽  
Hannah Cloke ◽  
Ervin Zsoter ◽  
Christel Prudhomme ◽  
Liz Stephens
Keyword(s):  
Tropical Cyclones ◽  
Southern Africa ◽  
Flood Hazard ◽  
Extreme Rainfall ◽  
Probability Of Detection ◽  
Forecast Skill ◽  
Management Service ◽  
Flood Events ◽  
Weather Forecasts ◽  
Mesoscale Convective

<p>Severe flooding in southern Africa is caused by a variety of meteorological hazards including intense tropical cyclones and depressions, mesoscale convective complexes and persistent lows which bring extreme rainfall and flood events with different characteristics. Little is known about the relative predictability of flooding associated to these different drivers, especially in operational forecasting systems. Understanding the limits of predictability for the different drivers of flooding is important to provide evidence of forecast capabilities to end-users and decision-makers and build trust in the use of the forecasting systems.</p><p>Here we explore the skill of probabilistic flood forecasts from the operational Copernicus Emergency Management Service Global Flood Awareness System (GloFAS v2) over southern Africa. GloFAS provides real-time hydrological forecasts up to 30 days ahead by coupling ensemble weather forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) with hydrological modelling. The GloFAS flood forecasts are openly available and can support humanitarians and other international organisations to trigger action before a devastating flood occurs.</p><p>Using hydrological records of past flood events over the last 20 years, the GloFAS forecast skill is assessed by analysing the probability of detection of the events over different lead-times from 1 to 30 days, as well as the consistency and accuracy of predictions of event-based characteristics such as the flood timing and duration. We stratify the analysis by the multi hazard drivers of flooding with a focus on the distinction between tropical cyclones and other types of meteorological events. We suggest that such a stratified analysis of forecast skill can help modellers better understand the sources of predictability in flood forecasts and can support humanitarians to define specific trigger levels for forecast-based action for different types of flood events.</p>

scholarly journals Rapid Cyclogenesis from a Mesoscale Frontal Wave on an Atmospheric River: Impacts on Forecast Skill and Predictability during Atmospheric River Landfall

2019 ◽  
Vol 20 (9) ◽  
pp. 1779-1794 ◽  
Author(s):  
Andrew C. Martin ◽  
F. Martin Ralph ◽  
Anna Wilson ◽  
Laurel DeHaan ◽  
Brian Kawzenuk
Keyword(s):  
Water Vapor ◽  
Lead Time ◽  
Forecast Skill ◽  
Vapor Transport ◽  
Lead Times ◽  
Forecast Uncertainty ◽  
Water Vapor Flux ◽  
Vapor Flux ◽  
Frontal Wave ◽  
Atmospheric River

Abstract Mesoscale frontal waves have the potential to modify the hydrometeorological impacts of atmospheric rivers (ARs). The small scale and rapid growth of these waves pose significant forecast challenges. We examined a frontal wave that developed a secondary cyclone during the landfall of an extreme AR in Northern California. We document rapid changes in significant storm features including integrated vapor transport and precipitation and connect these to high forecast uncertainty at 1–4-days’ lead time. We also analyze the skill of the Global Ensemble Forecast System in predicting secondary cyclogenesis and relate secondary cyclogenesis prediction skill to forecasts of AR intensity, AR duration, and upslope water vapor flux in the orographic controlling layer. Leveraging a measure of reference accuracy designed for cyclogenesis, we found forecasts were only able to skillfully predict secondary cyclogenesis for lead times less than 36 h. Forecast skill in predicting the large-scale pressure pattern and integrated vapor transport was lost by 96-h lead time. For lead times longer than 36 h, the failure to predict secondary cyclogenesis led to significant uncertainty in forecast AR intensity and to long bias in AR forecast duration. Failure to forecast a warm front associated with the secondary cyclone at lead times less than 36 h caused large overprediction of upslope water vapor flux, an important indicator of orographic precipitation forcing. This study highlights the need to identify offshore mesoscale frontal waves in real time and to characterize the forecast uncertainty inherent in these events when creating hydrometeorological forecasts.

Evaluation of the subseasonal forecast skill of floods associated with atmospheric rivers in coastal Western U.S. watersheds

2021 ◽  
Author(s):  
Qian Cao ◽  
Shraddhanand Shukla ◽  
Michael J. DeFlorio ◽  
F. Martin Ralph ◽  
Dennis P. Lettenmaier
Keyword(s):  
River Basin ◽  
West Coast ◽  
Forecast Skill ◽  
Probabilistic Forecast ◽  
Lead Times ◽  
Flood Events ◽  
Streamflow Prediction ◽  
Antecedent Soil Moisture ◽  
Atmospheric Rivers ◽  
The U.S

AbstractAtmospheric rivers (ARs) are responsible for up to 90% of major flood events along the U.S. West Coast. The timescale of subseasonal forecasting (two weeks to one month) is a critical lead time for proactive mitigation of flood disasters. The NOAA/Climate Testbed Subseasonal Experiment (SubX) is a research-to-operations project with almost immediate availability of forecasts. It has produced a reforecast database that facilitates evaluation of flood forecasts at these subseasonal lead times. Here, we examine the SubX driven forecast skill of AR-related flooding out to 4-week lead using the Distributed Hydrology Soil Vegetation Model (DHSVM), with particular attention to the role of antecedent soil moisture (ASM), which modulates the relationship between meteorological and hydrological forecast skill. We study three watersheds along a transect of the U.S. West Coast: the Chehalis River basin in Washington, the Russian River basin in Northern California, and the Santa Margarita River basin in Southern California. We find that the SubX driven flood forecast skill drops quickly after week 1, during which there is relatively high deterministic forecast skill. We find some probabilistic forecast skill relative to climatology as well as ensemble streamflow prediction (ESP) in week 2, but minimal skill in weeks 3-4, especially for annual maximum floods, notwithstanding some probabilistic skill for smaller floods in week 3. Using ESP and reverse-ESP experiments to consider the relative influence of ASM and SubX reforecast skill, we find that ASM dominates probabilistic forecast skill only for small flood events at week 1, while SubX reforecast skill dominates for large flood events at all lead times.

scholarly journals Evaluation and Error Correction of the ECMWF Subseasonal Precipitation Forecast over Eastern China during Summer

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Huanran He ◽  
Suxiang Yao ◽  
Anning Huang ◽  
Kejian Gong
Keyword(s):  
Error Correction ◽  
Forecast Error ◽  
Eastern China ◽  
Model Performance ◽  
Probability Of Detection ◽  
Forecast Skill ◽  
Precipitation Forecast ◽  
Lead Times ◽  
Ecmwf Model ◽  
Mean Square Errors

Subseasonal-to-seasonal (S2S) prediction is a highly regarded skill around the world. To improve the S2S forecast skill, an S2S prediction project and an extensive database have been established. In this study, the European Center for Medium-Range Weather Forecasts (ECMWF) model hindcast, which participates in the S2S prediction project, is systematically assessed by focusing on the hindcast quality for the summer accumulated ten-day precipitation at lead times of 0–30 days during 1995–2014 in eastern China. Additionally, the hindcast error is corrected by utilizing the preceding sea surface temperature (SST). The metrics employed to measure the ECMWF hindcast performance indicate that the ECMWF model performance drops as the lead time increases and exhibits strong interannual differences among the five subregions of eastern China. In addition, the precipitation forecast skill of the ECMWF hindcast is best at approximately 15 days in some areas of Southeast China; after correcting the forecast error, the forecast skill is increased to 30 days. At lead times of 0–30 days, regardless of whether the forecast error is corrected, the root mean square errors are lowest in Northeast China. After correcting the forecast error, the performance of the ECMWF hindcast shows better improvement in depicting the quantity and temporal and spatial variation of precipitation at lead times of 0–30 days in eastern China. The false alarm ratio (FAR), probability of detection (POD), and equitable threat score (ETS) reveal that the ECMWF model has a preferable performance at forecasting accumulated ten-day precipitation rates of approximately 20∼50 mm and indicates an improved hindcast quality after the forecast error correction. In short, adopting the preceding SST to correct the summer subseasonal precipitation of the ECMWF hindcast is preferable.

scholarly journals Evaluating CFSv2 Subseasonal Forecast Skill with an Emphasis on Tropical Convection

2017 ◽  
Vol 145 (9) ◽  
pp. 3795-3815 ◽  
Author(s):  
Nicholas J. Weber ◽  
Clifford F. Mass
Keyword(s):  
Lead Time ◽  
Large Scale ◽  
Tropical Convection ◽  
Forecast Skill ◽  
Lead Times ◽  
Forecast Errors ◽  
Predictive Skill ◽  
Forecast Lead Time ◽  
Temporal Averaging ◽  
Total Variability

This study examines the subseasonal predictive skill of CFSv2, focusing on the spatial and temporal distributions of error for large-scale atmospheric variables and the realism of simulated tropical convection. Errors in a 4-member CFSv2 ensemble forecast saturate at lead times of approximately 3 weeks for 500-hPa geopotential height and 5 weeks for 200-hPa velocity potential. Forecast errors exceed those of climatology at lead times beyond 2 weeks. Sea surface temperature, which evolves more slowly than atmospheric fields, maintains skill over climatology through the first month. Spatial patterns of error are robust across lead times and temporal averaging periods, increasing in amplitude as lead time increases and temporal averaging period decreases. Several significant biases were found in the CFSv2 reforecasts, such as too little convection over tropical land and excessive convection over the ocean. The realism of simulated tropical convection and associated teleconnections degrades with forecast lead time. Large-scale tropical convection in CFSv2 is more stationary than observed. Forecast MJOs propagate eastward too slowly and those initiated over the Indian Ocean have trouble traversing beyond the Maritime Continent. The total variability of simulated propagating convection is concentrated at lower frequencies compared to observed convection, and is more fully described by a red spectrum, indicating weak representation of convectively coupled waves. These flaws in simulated tropical convection, which could be tied to problems with convective parameterization and associated mean state biases, affect atmospheric teleconnections and may degrade extended global forecast skill.

scholarly journals Conditioning ensemble streamflow prediction with the North Atlantic Oscillation improves skill at longer lead times

2021 ◽  
Vol 25 (7) ◽  
pp. 4159-4183
Author(s):  
Seán Donegan ◽  
Conor Murphy ◽  
Shaun Harrigan ◽  
Ciaran Broderick ◽  
Dáire Foran Quinn ◽  
...  
Keyword(s):  
Water Resources ◽  
North Atlantic Oscillation ◽  
Water Resources Management ◽  
North Atlantic ◽  
Lead Time ◽  
Forecast Skill ◽  
Lead Times ◽  
Streamflow Prediction ◽  
Resources Management ◽  
Ensemble Streamflow Prediction

Abstract. Skilful hydrological forecasts can benefit decision-making in water resources management and other water-related sectors that require long-term planning. In Ireland, no such service exists to deliver forecasts at the catchment scale. In order to understand the potential for hydrological forecasting in Ireland, we benchmark the skill of ensemble streamflow prediction (ESP) for a diverse sample of 46 catchments using the GR4J (Génie Rural à 4 paramètres Journalier) hydrological model. Skill is evaluated within a 52-year hindcast study design over lead times of 1 d to 12 months for each of the 12 initialisation months, January to December. Our results show that ESP is skilful against a probabilistic climatology benchmark in the majority of catchments up to several months ahead. However, the level of skill was strongly dependent on lead time, initialisation month, and individual catchment location and storage properties. Mean ESP skill was found to decay rapidly as a function of lead time, with a continuous ranked probability skill score (CRPSS) of 0.8 (1 d), 0.32 (2-week), 0.18 (1-month), 0.05 (3-month), and 0.01 (12-month). Forecasts were generally more skilful when initialised in summer than other seasons. A strong correlation (ρ=0.94) was observed between forecast skill and catchment storage capacity (baseflow index), with the most skilful regions, the Midlands and the East, being those where slowly responding, high-storage catchments are located. Forecast reliability and discrimination were also assessed with respect to low- and high-flow events. In addition to our benchmarking experiment, we conditioned ESP with the winter North Atlantic Oscillation (NAO) using adjusted hindcasts from the Met Office's Global Seasonal Forecasting System version 5. We found gains in winter forecast skill (CRPSS) of 7 %–18 % were possible over lead times of 1 to 3 months and that improved reliability and discrimination make NAO-conditioned ESP particularly effective at forecasting dry winters, a critical season for water resources management. We conclude that ESP is skilful in a number of different contexts and thus should be operationalised in Ireland given its potential benefits for water managers and other stakeholders.

scholarly journals Tornado Probability of Detection and Lead Time as a Function of Convective Mode and Environmental Parameters

2013 ◽  
Vol 28 (5) ◽  
pp. 1261-1276 ◽  
Author(s):  
Jerald A. Brotzge ◽  
Steven E. Nelson ◽  
Richard L. Thompson ◽  
Bryan T. Smith
Keyword(s):  
Lead Time ◽  
Wind Shear ◽  
Convective Available Potential Energy ◽  
Vertical Wind Shear ◽  
Environmental Parameters ◽  
Probability Of Detection ◽  
Lead Times ◽  
Convective Systems ◽  
Tornado Warnings ◽  
Supercell Storms

Abstract The ability to provide advanced warning on tornadoes can be impacted by variations in storm mode. This research evaluates 2 yr of National Weather Service (NWS) tornado warnings, verification reports, and radar-derived convective modes to appraise the ability of the NWS to warn across a variety of convective modes and environmental conditions. Several specific hypotheses are considered: (i) supercell morphologies are the easiest convective modes to warn for tornadoes and yield the greatest lead times, while tornadoes from more linear, nonsupercell convective modes, such as quasi-linear convective systems, are more difficult to warn for; (ii) parameters such as tornado distance from radar, population density, and tornado intensity (F scale) introduce significant and complex variability into warning statistics as a function of storm mode; and (iii) tornadoes from stronger storms, as measured by their mesocyclone strength (when present), convective available potential energy (CAPE), vertical wind shear, and significant tornado parameter (STP) are easier to warn for than tornadoes from weaker systems. Results confirmed these hypotheses. Supercell morphologies caused 97% of tornado fatalities, 96% of injuries, and 92% of damage during the study period. Tornado warnings for supercells had a statistically higher probability of detection (POD) and lead time than tornado warnings for nonsupercells; among supercell storms, tornadoes from supercells in lines were slightly more difficult to warn for than tornadoes from discrete or clusters of supercells. F-scale intensity and distance from radar had some impact on POD, with less impact on lead times. Higher mesocyclone strength (when applicable), CAPE, wind shear, and STP values were associated with greater tornado POD and lead times.

scholarly journals Using ensemble reforecasts to generate flood thresholds for improved global flood forecasting

2021 ◽  
Author(s):  
Ervin Zsoter ◽  
Christel Prudhomme ◽  
Elisabeth Stephens ◽  
Hannah Cloke
Keyword(s):  
River Flow ◽  
Flood Forecasting ◽  
Lead Times ◽  
Management Service ◽  
Flood Events ◽  
Ensemble Forecasts ◽  
Forecast Lead Time ◽  
Extreme Flood ◽  
Definition Of ◽  
The Impact

<p>Global flood forecasting systems rely on definition of flood thresholds for identifying upcoming flood events. Existing methods for flood threshold definition can often be based on reanalysis datasets and single thresholds, used for all forecast lead times, but this leads to inconsistencies between how the extreme flood events are represented in the flood thresholds and the ensemble forecasts.</p><p>This paper explores the potential benefits of using river flow ensemble reforecasts to generate flood thresholds that can deliver improved reliability and skill. Using the Copernicus Emergency Management Service’s Global Flood Awareness System, the impact of the dataset and the method used to sample the annual maxima to define flood thresholds, are analysed in terms of threshold magnitude, forecast reliability and skill for different flood severity levels and lead times.</p><p>It was found that the variability of the threshold magnitudes, when estimated from the different annual maxima samples, can be extremely large, as can the subsequent impact on forecast skill. It was also found that reanalysis-based thresholds should only be used for the first few days, after which ensemble-reforecast-based thresholds, that vary with forecast lead time and can account for the forecast bias trends, provide more reliable and skilful flood forecasts.</p><p> </p><p> </p>

scholarly journals A Spatiotemporal Convolutional Gated Recurrent Unit Network for Mean Wave Period Field Forecasting

2021 ◽  
Vol 9 (4) ◽  
pp. 383
Author(s):  
Ting Yu ◽  
Jichao Wang
Keyword(s):  
Lead Time ◽  
Wave Period ◽  
Data Driven ◽  
Lead Times ◽  
Proposed Model ◽  
Data Driven Approach ◽  
Mean Wave Period ◽  
Scattering Index ◽  
Gated Recurrent Unit ◽  
Methods Numerical

Mean wave period (MWP) is one of the key parameters affecting the design of marine facilities. Currently, there are two main methods, numerical and data-driven methods, for forecasting wave parameters, of which the latter are widely used. However, few studies have focused on MWP forecasting, and even fewer have investigated it with spatial and temporal information. In this study, correlations between ocean dynamic parameters are explored to obtain appropriate input features, significant wave height (SWH) and MWP. Subsequently, a data-driven approach, the convolution gated recurrent unit (Conv-GRU) model with spatiotemporal characteristics, is utilized to field forecast MWP with 1, 3, 6, 12, and 24-h lead times in the South China Sea. Six points at different locations and six consecutive moments at every 12-h intervals are selected to study the forecasting ability of the proposed model. The Conv-GRU model has a better performance than the single gated recurrent unit (GRU) model in terms of root mean square error (RMSE), the scattering index (SI), Bias, and the Pearson’s correlation coefficient (R). With the lead time increasing, the forecast effect shows a decreasing trend, specifically, the experiment displays a relatively smooth forecast curve and presents a great advantage in the short-term forecast of the MWP field in the Conv-GRU model, where the RMSE is 0.121 m for 1-h lead time.

scholarly journals A pan-African medium-range ensemble flood forecast system

2015 ◽  
Vol 19 (8) ◽  
pp. 3365-3385 ◽  
Author(s):  
V. Thiemig ◽  
B. Bisselink ◽  
F. Pappenberger ◽  
J. Thielen
Keyword(s):  
Large Scale ◽  
Practical Implication ◽  
River Basins ◽  
Flood Forecasting ◽  
Small Scale ◽  
Flood Forecast ◽  
Flood Events ◽  
Forecast System ◽  
Pan African ◽  
Forecasting System

Abstract. The African Flood Forecasting System (AFFS) is a probabilistic flood forecast system for medium- to large-scale African river basins, with lead times of up to 15 days. The key components are the hydrological model LISFLOOD, the African GIS database, the meteorological ensemble predictions by the ECMWF (European Centre for Medium-Ranged Weather Forecasts) and critical hydrological thresholds. In this paper, the predictive capability is investigated in a hindcast mode, by reproducing hydrological predictions for the year 2003 when important floods were observed. Results were verified by ground measurements of 36 sub-catchments as well as by reports of various flood archives. Results showed that AFFS detected around 70 % of the reported flood events correctly. In particular, the system showed good performance in predicting riverine flood events of long duration (> 1 week) and large affected areas (> 10 000 km2) well in advance, whereas AFFS showed limitations for small-scale and short duration flood events. The case study for the flood event in March 2003 in the Sabi Basin (Zimbabwe) illustrated the good performance of AFFS in forecasting timing and severity of the floods, gave an example of the clear and concise output products, and showed that the system is capable of producing flood warnings even in ungauged river basins. Hence, from a technical perspective, AFFS shows a large potential as an operational pan-African flood forecasting system, although issues related to the practical implication will still need to be investigated.

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