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Hui-Ling Chang, Huiling Yuan, and Pay-Liam Lin

Abstract

This study pioneers the development of short-range (0–12 h) probabilistic quantitative precipitation forecasts (PQPFs) in Taiwan and aims to produce the PQPFs from time-lagged multimodel ensembles using the Local Analysis and Prediction System (LAPS). By doing so, the critical uncertainties in prediction processes can be captured and conveyed to the users. Since LAPS adopts diabatic data assimilation, it is utilized to mitigate the “spinup” problem and produce more accurate precipitation forecasts during the early prediction stage (0–6 h).

The LAPS ensemble prediction system (EPS) has a good spread–skill relationship and good discriminating ability. Therefore, though it is obviously wet biased, the forecast biases can be corrected to improve the skill of PQPFs through a linear regression (LR) calibration procedure. Sensitivity experiments for two important factors affecting calibration results are also conducted: the experiments on different training samples and the experiments on the accuracy of observation data. The first point reveals that the calibration results vary with training samples. Based on the statistical viewpoint, there should be enough samples for an effective calibration. Nevertheless, adopting more training samples does not necessarily produce better calibration results. It is essential to adopt training samples with similar forecast biases as validation samples to achieve better calibration results. The second factor indicates that as a result of the inconsistency of observation data accuracy in the sea and land areas, only separate calibration for these two areas can ensure better calibration results of the PQPFs.

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Hui-Ling Chang, Shu-Chih Yang, Huiling Yuan, Pay-Liam Lin, and Yu-Chieng Liou

Abstract

Measurement of the usefulness of numerical weather prediction considers not only the forecast quality but also the possible economic value (EV) in the daily decision-making process of users. Discrimination ability of an ensemble prediction system (EPS) can be assessed by the relative operating characteristic (ROC), which is closely related to the EV provided by the same forecast system.

Focusing on short-range probabilistic quantitative precipitation forecasts (PQPFs) for typhoons, this study demonstrates the consistent and strongly related characteristics of ROC and EV based on the Local Analysis and Prediction System (LAPS) EPS operated at the Central Weather Bureau in Taiwan. Sensitivity experiments including the effect of terrain, calibration, and forecast uncertainties on ROC and EV show that the potential EV provided by a forecast system is mainly determined by the discrimination ability of the same system. The ROC and maximum EV (EVmax) of an EPS are insensitive to calibration, but the optimal probability threshold to achieve the EVmax becomes more reliable after calibration. In addition, the LAPS ensemble probabilistic forecasts outperform deterministic forecasts in respect to both ROC and EV, and such an advantage grows with increasing precipitation intensity. Also, even without explicitly knowing the cost–loss ratio, one can still optimize decision-making and obtain the EVmax by using ensemble probabilistic forecasts.

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Chungu Lu, Huiling Yuan, Edward I. Tollerud, and Ning Wang
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Chungu Lu, Huiling Yuan, Barry E. Schwartz, and Stanley G. Benjamin

Abstract

A time-lagged ensemble forecast system is developed using a set of hourly initialized Rapid Update Cycle model deterministic forecasts. Both the ensemble-mean and probabilistic forecasts from this time-lagged ensemble system present a promising improvement in the very short-range weather forecasting of 1–3 h, which may be useful for aviation weather prediction and nowcasting applications. Two approaches have been studied to combine deterministic forecasts with different initialization cycles as the ensemble members. The first method uses a set of equally weighted time-lagged forecasts and produces a forecast by taking the ensemble mean. The second method adopts a multilinear regression approach to select a set of weights for different time-lagged forecasts. It is shown that although both methods improve short-range forecasts, the unequally weighted method provides the best results for all forecast variables at all levels. The time-lagged ensembles also provide a sample of statistics, which can be used to construct probabilistic forecasts.

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Isidora Jankov, Jian-Wen Bao, Paul J. Neiman, Paul J. Schultz, Huiling Yuan, and Allen B. White

Abstract

Numerical prediction of precipitation associated with five cool-season atmospheric river events in northern California was analyzed and compared to observations. The model simulations were performed by using the Advanced Research Weather Research and Forecasting Model (ARW-WRF) with four different microphysical parameterizations. This was done as a part of the 2005–06 field phase of the Hydrometeorological Test Bed project, for which special profilers, soundings, and surface observations were implemented. Using these unique datasets, the meteorology of atmospheric river events was described in terms of dynamical processes and the microphysical structure of the cloud systems that produced most of the surface precipitation. Events were categorized as “bright band” (BB) or “nonbright band” (NBB), the differences being the presence of significant amounts of ice aloft (or lack thereof) and a signature of higher reflectivity collocated with the melting layer produced by frozen precipitating particles descending through the 0°C isotherm.

The model was reasonably successful at predicting the timing of surface fronts, the development and evolution of low-level jets associated with latent heating processes and terrain interaction, and wind flow signatures consistent with deep-layer thermal advection. However, the model showed the tendency to overestimate the duration and intensity of the impinging low-level winds. In general, all model configurations overestimated precipitation, especially in the case of BB events. Nonetheless, large differences in precipitation distribution and cloud structure among model runs using various microphysical parameterization schemes were noted.

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Huiling Yuan, Steven L. Mullen, Xiaogang Gao, Soroosh Sorooshian, Jun Du, and Hann-Ming Henry Juang

Abstract

The National Centers for Environmental Prediction (NCEP) Regional Spectral Model (RSM) is used to produce twice-daily (0000 and 1200 UTC), high-resolution ensemble forecasts to 24 h. The forecasts are performed at an equivalent horizontal grid spacing of 12 km for the period 1 November 2002 to 31 March 2003 over the southwest United States. The performance of 6-h accumulated precipitation is assessed for 32 U.S. Geological Survey hydrologic catchments. Multiple accuracy and skill measures are used to evaluate probabilistic quantitative precipitation forecasts. NCEP stage-IV precipitation analyses are used as “truth,” with verification performed on the stage-IV 4-km grid. The RSM ensemble exhibits a ubiquitous wet bias. The bias manifests itself in areal coverage, frequency of occurrence, and total accumulated precipitation over every region and during every 6-h period. The biases become particularly acute starting with the 1800–0000 UTC interval, which leads to a spurious diurnal cycle and the 1200 UTC cycle being more adversely affected than the 0000 UTC cycle. Forecast quality and value exhibit marked variability over different hydrologic regions. The forecasts are highly skillful along coastal California and the windward slopes of the Sierra Nevada Mountains, but they generally lack skill over the Great Basin and the Colorado basin except over mountain peaks. The RSM ensemble is able to discriminate precipitation events and provide useful guidance to a wide range of users over most regions of California, which suggests that mitigation of the conditional biases through statistical postprocessing would produce major improvements in skill.

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Huiling Yuan, Xiaogang Gao, Steven L. Mullen, Soroosh Sorooshian, Jun Du, and Hann-Ming Henry Juang

Abstract

A feed-forward neural network is configured to calibrate the bias of a high-resolution probabilistic quantitative precipitation forecast (PQPF) produced by a 12-km version of the NCEP Regional Spectral Model (RSM) ensemble forecast system. Twice-daily forecasts during the 2002–2003 cool season (1 November–31 March, inclusive) are run over four U.S. Geological Survey (USGS) hydrologic unit regions of the southwest United States. Calibration is performed via a cross-validation procedure, where four months are used for training and the excluded month is used for testing. The PQPFs before and after the calibration over a hydrological unit region are evaluated by comparing the joint probability distribution of forecasts and observations. Verification is performed on the 4-km stage IV grid, which is used as “truth.” The calibration procedure improves the Brier score (BrS), conditional bias (reliability) and forecast skill, such as the Brier skill score (BrSS) and the ranked probability skill score (RPSS), relative to the sample frequency for all geographic regions and most precipitation thresholds. However, the procedure degrades the resolution of the PQPFs by systematically producing more forecasts with low nonzero forecast probabilities that drive the forecast distribution closer to the climatology of the training sample. The problem of degrading the resolution is most severe over the Colorado River basin and the Great Basin for relatively high precipitation thresholds where the sample of observed events is relatively small.

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Huiling Yuan, Steven L. Mullen, Xiaogang Gao, Soroosh Sorooshian, Jun Du, and Hann-Ming Henry Juang

Abstract

The National Centers for Environmental Prediction (NCEP) Regional Spectral Model (RSM) is used to generate ensemble forecasts over the southwest United States during the 151 days of 1 November 2002 to 31 March 2003. RSM forecasts to 24 h on a 12-km grid are produced from 0000 and 1200 UTC initial conditions. Eleven ensemble members are run each forecast cycle from the NCEP Global Forecast System (GFS) ensemble analyses (one control and five pairs of bred modes) and forecast lateral boundary conditions. The model domain covers two NOAA River Forecast Centers: the California Nevada River Forecast Center (CNRFC) and the Colorado Basin River Forecast Center (CBRFC). Ensemble performance is evaluated for probabilistic forecasts of 24-h accumulated precipitation in terms of several accuracy and skill measures. Differences among several NCEP precipitation analyses are assessed along with their impact on model verification, with NCEP stage IV blended analyses selected to represent “truth.”

Forecast quality and potential value are found to depend strongly on the verification dataset, geographic region, and precipitation threshold. In general, the RSM forecasts are skillful over the CNRFC region for thresholds between 1 and 50 mm but are unskillful over the CBRFC region. The model exhibits a wet bias for all thresholds that is larger over Nevada and the CBRFC region than over California. Mitigation of such biases over the Southwest will pose serious challenges to the modeling community in view of the uncertainties inherent in verifying analyses.

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Huiling Yuan, John A. McGinley, Paul J. Schultz, Christopher J. Anderson, and Chungu Lu

Abstract

High-resolution (3 km) time-lagged (initialized every 3 h) multimodel ensembles were produced in support of the Hydrometeorological Testbed (HMT)-West-2006 campaign in northern California, covering the American River basin (ARB). Multiple mesoscale models were used, including the Weather Research and Forecasting (WRF) model, Regional Atmospheric Modeling System (RAMS), and fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5). Short-range (6 h) quantitative precipitation forecasts (QPFs) and probabilistic QPFs (PQPFs) were compared to the 4-km NCEP stage IV precipitation analyses for archived intensive operation periods (IOPs). The two sets of ensemble runs (operational and rerun forecasts) were examined to evaluate the quality of high-resolution QPFs produced by time-lagged multimodel ensembles and to investigate the impacts of ensemble configurations on forecast skill. Uncertainties in precipitation forecasts were associated with different models, model physics, and initial and boundary conditions. The diabatic initialization by the Local Analysis and Prediction System (LAPS) helped precipitation forecasts, while the selection of microphysics was critical in ensemble design. Probability biases in the ensemble products were addressed by calibrating PQPFs. Using artificial neural network (ANN) and linear regression (LR) methods, the bias correction of PQPFs and a cross-validation procedure were applied to three operational IOPs and four rerun IOPs. Both the ANN and LR methods effectively improved PQPFs, especially for lower thresholds. The LR method outperformed the ANN method in bias correction, in particular for a smaller training data size. More training data (e.g., one-season forecasts) are desirable to test the robustness of both calibration methods.

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Huiling Yuan, Chungu Lu, John A. McGinley, Paul J. Schultz, Brian D. Jamison, Linda Wharton, and Christopher J. Anderson

Abstract

Short-range quantitative precipitation forecasts (QPFs) and probabilistic QPFs (PQPFs) are investigated for a time-lagged multimodel ensemble forecast system. One of the advantages of such an ensemble forecast system is its low-cost generation of ensemble members. In conjunction with a frequently cycling data assimilation system using a diabatic initialization [such as the Local Analysis and Prediction System (LAPS)], the time-lagged multimodel ensemble system offers a particularly appealing approach for QPF and PQPF applications. Using the NCEP stage IV precipitation analyses for verification, 6-h QPFs and PQPFs from this system are assessed during the period of March–May 2005 over the west-central United States. The ensemble system was initialized by hourly LAPS runs at a horizontal resolution of 12 km using two mesoscale models, including the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) and the Weather Research and Forecast (WRF) model with the Advanced Research WRF (ARW) dynamic core. The 6-h PQPFs from this system provide better performance than the NCEP operational North American Mesoscale (NAM) deterministic runs at 12-km resolution, even though individual members of the MM5 or WRF models perform comparatively worse than the NAM forecasts at higher thresholds and longer lead times. Recalibration was conducted to reduce the intensity errors in time-lagged members. In spite of large biases and spatial displacement errors in the MM5 and WRF forecasts, statistical verification of QPFs and PQPFs shows more skill at longer lead times by adding more members from earlier initialized forecast cycles. Combing the two models only reduced the forecast biases. The results suggest that further studies on time-lagged multimodel ensembles for operational forecasts are needed.

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