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

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.

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Evaluation of WRF Model Forecasts and Their Use for Hydroclimate Monitoring over Southern South America

Weather and Forecasting ◽

10.1175/waf-d-15-0130.1 ◽

2016 ◽

Vol 31 (3) ◽

pp. 1001-1017 ◽

Cited By ~ 3

Author(s):

Omar V. Müller ◽

Miguel A. Lovino ◽

Ernesto H. Berbery

Keyword(s):

South America ◽

Lead Time ◽

Weather Forecasting ◽

Wrf Model ◽

Probability Of Detection ◽

Southern South America ◽

Climate Conditions ◽

Forecast Lead Time ◽

Highly Correlated ◽

Precipitation Season

Abstract Weather forecasting and monitoring systems based on regional models are becoming increasingly relevant for decision support in agriculture and water management. This work evaluates the predictive and monitoring capabilities of a system based on WRF Model simulations at 15-km grid spacing over the La Plata basin (LPB) in southern South America, where agriculture and water resources are essential. The model’s skill up to a lead time of 7 days is evaluated with daily precipitation and 2-m temperature in situ observations for the 2-yr period from 1 August 2012 to 31 July 2014. Results show high prediction performance with 7-day lead time throughout the domain and particularly over LPB, where about 70% of rain and no-rain days are correctly predicted. Also, the probability of detection of rain days is above 80% in humid regions. Temperature observations and forecasts are highly correlated (r > 0.80) while mean absolute errors, even at the maximum lead time, remain below 2.7°C for minimum and mean temperatures and below 3.7°C for maximum temperatures. The usefulness of WRF products for hydroclimate monitoring was tested for an unprecedented drought in southern Brazil and for a slightly above normal precipitation season in northeastern Argentina. In both cases the model products reproduce the observed precipitation conditions with consistent impacts on soil moisture, evapotranspiration, and runoff. This evaluation validates the model’s usefulness for forecasting weather up to 1 week in advance and for monitoring climate conditions in real time. The scores suggest that the forecast lead time can be extended into a second week, while bias correction methods can reduce some of the systematic errors.

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ESTIMATIVA DA PROBABILIDADE DE OCORRÊNCIA DE PRECIPITAÇÃO, A PARTIR DE TÉCNICAS ESTATÍSTICAS NÃO PARAMÉTRICAS APLICADAS A SIMULAÇÕES NUMÉRICAS DE WRF. UM CASO DE ESTUDO

Ciência e Natura ◽

10.5902/2179460x20193 ◽

2016 ◽

Vol 38 ◽

pp. 491

Author(s):

Lissette Guzmán Rodríguez ◽

Vagner Anabor ◽

Franciano Scremin Puhales ◽

Everson Dal Piva

Keyword(s):

Wrf Model ◽

Random Variable ◽

Heavy Rain ◽

Heavy Precipitation ◽

Precipitation Forecast ◽

Precipitation Event ◽

Ensemble Prediction ◽

False Alarms ◽

Heavy Precipitation Event ◽

Skill Scores

In this paper was used the kernel density estimation (KDE), a nonparametric method to estimate the probability density function of a random variable, to obtain a probabilistic precipitation forecast, from an ensemble prediction with the WRF model. The nine members of the prediction were obtained by varying the convective parameterization of the model, for a heavy precipitation event in southern Brazil. Evaluating the results, the estimated probabilities obtained for periods of 3 and 24 hours, and various thresholds of precipitation, were compared with the estimated precipitation of the TRMM, without showing a clear morphological correspondence between them. For accumulated in 24 hours, it was possible to compare the specific values of the observations of INMET, finding better coherence between the observations and the predicted probabilities. Skill scores were calculated from contingency tables, for different ranks of probabilities, and the forecast of heavy rain had higher proportion correct in all ranks of probabilities, and forecasted precipitation with probability of 75%, for any threshold, did not produce false alarms. Furthermore, the precipitation of lower intensity with marginal probability was over-forecasted, showing also higher index of false alarms.

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Triggering Mechanism of an Extreme Rainstorm Process near the Tianshan Mountains in Xinjiang, an Arid Region in China, Based on a Numerical Simulation

Advances in Meteorology ◽

10.1155/2020/8828060 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Yong Zeng ◽

Lianmei Yang

Keyword(s):

Water Vapor ◽

Arid Region ◽

Middle Atmosphere ◽

Wrf Model ◽

Heavy Rain ◽

Cloud Water ◽

Tianshan Mountains ◽

Triggering Mechanism ◽

Cold Air ◽

Convergence Line

The current study investigated the triggering mechanism of a record-breaking heavy rain process in the area near the Tianshan Mountains in Xinjiang, an arid region in China, from July 31 to August 1, 2016, based on the simulation using the Weather Research and Forecasting (WRF) model. The results illustrated that the rainstorm system was generated in the middle atmosphere of the western Aksu region near the Tianshan Mountains and gradually evolved into a multicell linear echo during system evolution. The cold air transported from the Tianshan Mountains partly reached the low altitudes during the downhill process, and the warm southwest air from Aksu was lifted, forming oblique updraft airflow. The other part of the cold air converged with the southeastern warm air in the middle atmosphere, and the transportation and convergence of the water vapor related to the southwestern, southeastern, and oblique updraft airflows provided good water vapor conditions for the storm system. Meanwhile, the inclined upward air transported cloud water and ice-phase particles to high altitudes, mixing the two and generating a large amount of supercooled cloud water, which was very beneficial for the development and maintenance of the storm system. These conditions were favorable for power, heat, water vapor, and water condensate particles, which enabled the development and maintenance of the rainstorm system on the convergence line, thus triggering this rare rainstorm process during the movement to the northeast.

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

Journal of Hydrometeorology ◽

10.1175/jhm-d-18-0046.1 ◽

2018 ◽

Vol 19 (8) ◽

pp. 1289-1304 ◽

Cited By ~ 6

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.

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Contributions of Mixed Physics versus Perturbed Initial/Lateral Boundary Conditions to Ensemble-Based Precipitation Forecast Skill

Monthly Weather Review ◽

10.1175/2007mwr2029.1 ◽

2008 ◽

Vol 136 (6) ◽

pp. 2140-2156 ◽

Cited By ~ 36

Author(s):

Adam J. Clark ◽

William A. Gallus ◽

Tsing-Chang Chen

Keyword(s):

Lead Time ◽

Ensemble Forecast ◽

Forecast Skill ◽

Precipitation Forecast ◽

Forecast Errors ◽

Lateral Boundary ◽

Model Errors ◽

Forecast Lead Time ◽

Statistical Consistency ◽

Made In

Abstract An experiment is described that is designed to examine the contributions of model, initial condition (IC), and lateral boundary condition (LBC) errors to the spread and skill of precipitation forecasts from two regional eight-member 15-km grid-spacing Weather Research and Forecasting (WRF) ensembles covering a 1575 km × 1800 km domain. It is widely recognized that a skillful ensemble [i.e., an ensemble with a probability distribution function (PDF) that generates forecast probabilities with high resolution and reliability] should account for both error sources. Previous work suggests that model errors make a larger contribution than IC and LBC errors to forecast uncertainty in the short range before synoptic-scale error growth becomes nonlinear. However, in a regional model with unperturbed LBCs, the infiltration of the lateral boundaries will negate increasing spread. To obtain a better understanding of the contributions to the forecast errors in precipitation and to examine the window of forecast lead time before unperturbed ICs and LBCs begin to cause degradation in ensemble forecast skill, the “perfect model” assumption is made in an ensemble that uses perturbed ICs and LBCs (PILB ensemble), and the “perfect analysis” assumption is made in another ensemble that uses mixed physics–dynamic cores (MP ensemble), thus isolating the error contributions. For the domain and time period used in this study, unperturbed ICs and LBCs in the MP ensemble begin to negate increasing spread around forecast hour 24, and ensemble forecast skill as measured by relative operating characteristic curves (ROC scores) becomes lower in the MP ensemble than in the PILB ensemble, with statistical significance beginning after forecast hour 69. However, degradation in forecast skill in the MP ensemble relative to the PILB ensemble is not observed in an analysis of deterministic forecasts calculated from each ensemble using the probability matching method. Both ensembles were found to lack statistical consistency (i.e., to be underdispersive), with the PILB ensemble (MP ensemble) exhibiting more (less) statistical consistency with respect to forecast lead time. Spread ratios in the PILB ensemble are greater than those in the MP ensemble at all forecast lead times and thresholds examined; however, ensemble variance in the MP ensemble is greater than that in the PILB ensemble during the first 24 h of the forecast. This discrepancy in spread measures likely results from greater bias in the MP ensemble leading to an increase in ensemble variance and decrease in spread ratio relative to the PILB ensemble.

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Probabilistic Precipitation Forecast Skill as a Function of Ensemble Size and Spatial Scale in a Convection-Allowing Ensemble

Monthly Weather Review ◽

10.1175/2010mwr3624.1 ◽

2011 ◽

Vol 139 (5) ◽

pp. 1410-1418 ◽

Cited By ~ 89

Author(s):

Adam J. Clark ◽

John S. Kain ◽

David J. Stensrud ◽

Ming Xue ◽

Fanyou Kong ◽

...

Keyword(s):

Spatial Scale ◽

Lead Time ◽

Spatial Scales ◽

Characteristic Curve ◽

Careful Consideration ◽

Precipitation Forecast ◽

Ensemble Size ◽

Relative Operating Characteristic ◽

Forecast Lead Time ◽

Roc Area

Probabilistic quantitative precipitation forecasts (PQPFs) from the storm-scale ensemble forecast system run by the Center for Analysis and Prediction of Storms during the spring of 2009 are evaluated using area under the relative operating characteristic curve (ROC area). ROC area, which measures discriminating ability, is examined for ensemble size n from 1 to 17 members and for spatial scales ranging from 4 to 200 km. Expectedly, incremental gains in skill decrease with increasing n. Significance tests comparing ROC areas for each n to those of the full 17-member ensemble revealed that more members are required to reach statistically indistinguishable PQPF skill relative to the full ensemble as forecast lead time increases and spatial scale decreases. These results appear to reflect the broadening of the forecast probability distribution function (PDF) of future atmospheric states associated with decreasing spatial scale and increasing forecast lead time. They also illustrate that efficient allocation of computing resources for convection-allowing ensembles requires careful consideration of spatial scale and forecast length desired.

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

Journal of Hydrometeorology ◽

10.1175/jhm-d-12-0129.1 ◽

2013 ◽

Vol 14 (4) ◽

pp. 1075-1097 ◽

Cited By ~ 23

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.

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Calibrated Precipitation Forecasts for a Limited-Area Ensemble Forecast System Using Reforecasts

Monthly Weather Review ◽

10.1175/2009mwr2977.1 ◽

2010 ◽

Vol 138 (1) ◽

pp. 176-189 ◽

Cited By ~ 36

Author(s):

Felix Fundel ◽

Andre Walser ◽

Mark A. Liniger ◽

Christoph Frei ◽

Christof Appenzeller

Keyword(s):

Return Period ◽

Lead Time ◽

Precipitation Forecast ◽

Ensemble Prediction ◽

Small Scale ◽

Limited Area ◽

Return Periods ◽

Model Errors ◽

Ensemble Prediction System ◽

Forecast Lead Time

Abstract The calibration of numerical weather forecasts using reforecasts has been shown to increase the skill of weather predictions. Here, the precipitation forecasts from the Consortium for Small Scale Modeling Limited Area Ensemble Prediction System (COSMO-LEPS) are improved using a 30-yr-long set of reforecasts. The probabilistic forecasts are calibrated on the exceedance of return periods, independently from available observations. Besides correcting for systematic model errors, the spatial and temporal variability in the amplitude of rare precipitation events is implicitly captured when issuing forecasts of return periods. These forecast products are especially useful for issuing warnings of upcoming events. A way to visualize those calibrated ensemble forecasts conveniently for end users and to present verification results of the return period–based forecasts for Switzerland is proposed. It is presented that, depending on the lead time and return period, calibrating COSMO-LEPS with reforecasts increases the precipitation forecast skill substantially (about 1 day in forecast lead time). The largest improvements are achieved during winter months. The reasonable choice of the length of the reforecast climatology is estimated for an efficient use of this computational expensive calibration method.

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

Monthly Weather Review ◽

10.1175/2010mwr3217.1 ◽

2010 ◽

Vol 138 (10) ◽

pp. 3787-3805 ◽

Cited By ~ 17

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.

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Simulation of karst floods with a hydrological model improved by meteorological model coupling

Journal of Hydrometeorology ◽

10.1175/jhm-d-21-0088.1 ◽

2021 ◽

Keyword(s):

Flood Control ◽

Underground Structure ◽

Wrf Model ◽

Precipitation Forecast ◽

Karst Area ◽

Peak Time ◽

Flood Prediction ◽

Meteorological Model ◽

Flood Peak ◽

Flood Warning

Abstract Karst basins are prone to rapid flooding because of their geomorphic complexity and exposed karst landforms with low infiltration rates. Accordingly, simulating and forecasting floods in karst regions can provide important technical support for local flood control. The study area, the Liujiang karst river basin, is the most well-developed karst area in South China, and its many mountainous areas lack rainfall gauges, limiting the availability of precipitation information. Quantitative precipitation forecast (QPF) from the Weather Research and Forecasting model (WRF) and quantitative precipitation estimation (QPE) from remote sensing information by an artificial neural network cloud classification system (PERSIANN-CCS) can offer reliable precipitation estimates. Here, the distributed Karst-Liuxihe (KL) model was successfully developed from the terrestrial Liuxihe model, as reflected in improvements to its underground structure and confluence algorithm. Compared with other karst distributed models, the KL model has a relatively simple structure and small modeling data requirements, which are advantageous for flood prediction in karst areas lacking hydrogeological data. Our flood process simulation results suggested that the KL model agrees well with observations and outperforms the Liuxihe model. The average Nash coefficient, correlation coefficient, and water balance coefficient increased by 0.24, 0.19, and 0.20, respectively, and the average flood process error, flood peak error, and peak time error decreased by 13%, 11%, and 2 hours, respectively. Coupling the WRF model and PERSIANN-CCS with the KL model yielded a good performance in karst flood simulation and prediction. Notably, coupling the WRF and KL models effectively predicted the karst flood processes and provided flood prediction results with a lead time of 96 hours, which is important for flood warning and control.

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