High-Resolution GFS-Based MOS Quantitative Precipitation Forecasts on a 4-km Grid

Charba, Jerome P.; Samplatsky, Frederick G.

doi:10.1175/2010mwr3224.1

Cited by 23 publications

(15 citation statements)

References 30 publications

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“…As listed in Table 4, the mean TS values from day-1 QPFs by the 2.5-km CReSS for the top-5 cases at the thresholds of 25 mm (near 1 in.) Although not as high as the top-5 cases, they are already much higher than typical values and longterm averages in many places (e.g., Olson et al 1995;Mesinger 1996;Ralph et al 2005;Marchok et al 2007;Tuleya et al 2007;Charba and Samplatsky 2011). The day-2 and day-3 QPFs that started 24 and 48 h earlier register mean TSs of 0.75, 0.73, 0.57, 0.42, and 0.17, and 0.64, 0.57, 0.37, 0.33, and 0.22 at the same thresholds, respectively (cf.…”

Section: A Model Performance For Hazardous and Extreme Eventsmentioning

confidence: 72%

The More Rain, the Better the Model Performs—The Dependency of Quantitative Precipitation Forecast Skill on Rainfall Amount for Typhoons in Taiwan

Wang

2015

Monthly Weather Review

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A strong dependency of model performance in quantitative precipitation forecasts (QPFs) as measured by scores such as the threat score (TS) on rainfall amount (i.e., the better the model performs when there is more rain), is demonstrated through real-time forecasts by the 2.5-km Cloud-Resolving Storm Simulator (CReSS) for 15 typhoons in Taiwan in 2010-12. Implied simply from the positive correlation between rain-area sizes and scores, this dependency is expected to exist in all regions, models, and rainfall regimes, while for typhoon QPFs in Taiwan it is also attributed to the model's capability to properly handle (within 72 h) the processes leading to more rain, which are largely controlled by the typhoon's track, size, structure, and environment, and the island's topography. Because of this dependency, the performance of model QPFs for extreme events can be assessed accurately only when forecasts targeted for periods of comparable rainfall magnitude are included for averaging. For the most-rainy 24 h of the top-5 typhoons, the 0-24-h QPFs by CReSS have mean TS of 0.67, 0.67, 0.58, 0.51, and 0.32 at thresholds of 25, 50, 130, 200, and 350 mm, and 0.64, 0.57, 0.37, 0.33, and 0.22 from 48-72-h QPFs, respectively, suggesting superior performance even 2-2.5 days in advance. These scores are strikingly high, precisely because Taiwan can receive extreme rainfall from typhoons. For smaller (nonhazardous) events, the mean scores are progressively lower, but also unimportant and less representative statistically. Therefore, it is inappropriate to average scores over multiple forecasts as those for less-rainy periods would contaminate the result for key periods. The implication for forecasters is also discussed.

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Section: A Model Performance For Hazardous and Extreme Eventsmentioning

confidence: 72%

The More Rain, the Better the Model Performs—The Dependency of Quantitative Precipitation Forecast Skill on Rainfall Amount for Typhoons in Taiwan

Wang

2015

Monthly Weather Review

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“…Ensemble forecasts of precipitation are replacing single (deterministic) forecasts for extending streamflow predictions beyond 48 hours. However, improvements in the prediction of precipitation have lagged behind the much more significant improvements made by operational NWP models in forecasting many aspects of the largescale circulation [1]. For example, [4] found using the Global Forecast System (GFS) the precipitation total was underestimated and that the spatial distribution of the rainfall was degraded by the resolution of the global model.…”

Section: Related Workmentioning

confidence: 99%

Towards long-lead forecasting of extreme flood events

Wang¹,

Ding²,

et al. 2013

Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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The development of disastrous flood forecasting techniques able to provide warnings at a long lead-time (5-15 days) is of great importance to society. Extreme Flood is usually a consequence of a sequence of precipitation events occurring over from several days to several weeks. Though precise shortterm forecasting the magnitude and extent of individual precipitation event is still beyond our reach, long-term forecasting of precipitation clusters can be attempted by identifying persistent atmospheric regimes that are conducive for the precipitation clusters. However, such forecasting will suffer from overwhelming number of relevant features and high imbalance of sample sets. In this paper, we propose an integrated data mining framework for identifying the precursors to precipitation event clusters and use this information to predict extended periods of extreme precipitation and subsequent floods. We synthesize a representative feature set that describes the atmosphere motion, and apply a streaming feature selection algorithm to online identify the precipitation precursors from the enormous feature space. A hierarchical * Corresponding Author ...$15.00.re-sampling approach is embedded in the framework to deal with the imbalance problem.An extensive empirical study is conducted on historical precipitation and associated flood data collected in the State of Iowa. Utilizing our framework a few physically meaningful precipitation cluster precursor sets are identified from millions of features. More than 90% of extreme precipitation events are captured by the proposed prediction model using precipitation cluster precursors with a lead time of more than 5 days.

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“…Through operational use, the sources of errors consist of: (1) Z bias including ground clutter, anomalous propagation, signal attenuation, beam blockage, bright band (BB) and variation of the vertical profile of reflectivity (VPR), (2) R bias including changes in the precipitation before reaching the ground and (3) inappropriate Z-R relationships (Villarini and Krajewski, 2010; Charba and * Correspondence: W. Zhang, Institute of Automation, Chinese Academy of Sciences, Beijing, China. E-mail: zhangwenshengia@hotmail.com Samplatsky, 2011;Shucksmith et al, 2011;Erdin et al, 2012;Lee et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

A terrain‐based weighted random forests method for radar quantitative precipitation estimation

Yang

Kuang

Zhang

et al. 2017

Meteorological Applications

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While weather radar is widely used for quantitative precipitation estimation (QPE) in China and many other countries, the performance of radar QPE is unsatisfactory. A major reason for inaccurate radar QPE is the application of conventional Z–R relationships. In this study the entire vertical profile of reflectivity (VPR) is taken into consideration and a new relationship converting the VPR to rainfall rate is developed. The new relationship is obtained by a proposed terrain‐based weighted random forests (TWRF) method. The TWRF method regards 21 levels of constant altitude plan position indicator reflectivity (from 1 to 18 km) as features. The method consists of two parts: the first is to obtain subregions based on terrain, and the second is to refine the classical random forests method by computing feature weights based on correlation co‐efficients between features and rainfall rate. Radar QPE based on the TWRF method was tested within the 45–100 km range of the radar in Hangzhou, China, on rainfall events in 2014. The proposed method showed improved performance for all verification scores over the Z–R relationship and the classical random forests method. Use of the entire VPR and the terrain‐based study proved to be effective in this example. Experimental results indicate that the proposed TWRF method can improve the accuracy of radar QPE compared to an independent network of rain gauges.

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High-Resolution GFS-Based MOS Quantitative Precipitation Forecasts on a 4-km Grid

Cited by 23 publications

References 30 publications

The More Rain, the Better the Model Performs—The Dependency of Quantitative Precipitation Forecast Skill on Rainfall Amount for Typhoons in Taiwan

The More Rain, the Better the Model Performs—The Dependency of Quantitative Precipitation Forecast Skill on Rainfall Amount for Typhoons in Taiwan

Towards long-lead forecasting of extreme flood events

A terrain‐based weighted random forests method for radar quantitative precipitation estimation

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