Evaluation of Rainfall Forecasts by Three Mesoscale Models during the Mei-yu Season of 2008 in Taiwan. Part I: Subjective Comparison

Paul, Sahana; Wang, Chung Chieh; Tseng, Lishan; Lee, Dong In; Hong, Jiman; Leou, Tzay Ming

doi:10.1007/s13143-021-00229-2

Cited by 3 publications

(16 citation statements)

References 67 publications

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“…Thus, this apparent over-forecast by CReSS in rainfall intensity deserves to be explored further also using other verification methods. The results obtained with this further investigation are reported in Part I [24] and Part III [49].…”

Section: Evaluation On Object Properties Without Matchingmentioning

confidence: 89%

“…Central Weather Bureau (CWB) rainfall data: In this study, gauge data from the dense network of the Automatic Rainfall and Meteorological Telemetric System (ARMTS, [39,40]) are used (Figure 1 in Paul et al [24]). Established and operated by the CWB, the ARMTS network includes 386 rain-gauge stations in 2008.…”

Section: Observation Datamentioning

confidence: 99%

“…Established and operated by the CWB, the ARMTS network includes 386 rain-gauge stations in 2008. The details of the data are described in Paul et al [24] (hereafter, Part I), and thus, only necessary information is given here.…”

Section: Observation Datamentioning

confidence: 99%

“…Provided by TRMM Multi-Satellite Precipitation Analysis (TMPA), this dataset (3B42) has a resolution of 0.25 • × 0.25 • every 3 h and employs a calibration-based sequential scheme to combine precipitation estimates from multiple satellites with gauge analyses at feasible locations (Huffman et al [41]). The readers are referred to Part I for details [24].…”

Section: Observation Datamentioning

confidence: 99%

“…The CWB gauge data and TRMM data are combined by means of the Cressman method [42], which is an objective analysis scheme to interpolate data onto grid points using a weighting function inversely proportional to distance. Here, the objective analysis is performed on the Cloud-Resolving Storm Simulator (CReSS) grid (to be described below) to merge ARMTS and TRMM data at 3-h intervals (also Paul et al [24]). To better preserve the high resolution of ARMTS data, four rounds of analysis with an increasingly larger radius of influence (7.5, 15, 30, and 60 km, respectively) are used in the Cressman scheme (with higher priority in earlier rounds) for grid points over Taiwan.…”

Section: Observation Datamentioning

confidence: 99%

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Evaluation of Rainfall Forecasts by Three Mesoscale Models during the Mei-yu Season of 2008 in Taiwan. Part II: Development of an Object-Oriented Method

2020

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This study describes a recently developed object-oriented method suitable for Taiwan for the purpose to verify quantitative precipitation forecasts (QPFs) produced by mesoscale models as a complement to the traditional approaches in existence. Using blended data from the rain-gauge network in Taiwan and the Tropical Rainfall Measuring Mission (TRMM) as the observation, the method developed herein is applied to twice-daily 0–48 h QPFs produced by the Cloud-Resolving Storm Simulator (CReSS) during the South-West Monsoon Experiment (SoWMEX) in May–June 2008. In this method, rainfall objects are identified through a procedure that includes smoothing and thresholding. Various attribute parameters and the characteristics of observed and forecast rain-area objects are then compared and discussed. Both the observed and the QPF frequency distributions of rain-area objects with respect to total water production, object size, and rainfall are similar to chi-distribution, with highest frequency at smaller values and decreased frequencies toward greater values. The model tends to produce heavier rainfall than observation, while the latter exhibits a higher percentage of larger objects with weaker rainfall intensity. The distributions of shape-related attributes are similar between QPF and observed rainfall objects, with more northeast–southwest oriented and fewer northwest–southeast oriented objects. Both observed and modeled object centroid locations have relative maxima over the terrain of Taiwan, indicating reasonable response to the topography. The above results are consistent with previous studies.

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Section: Evaluation On Object Properties Without Matchingmentioning

confidence: 89%

Section: Observation Datamentioning

confidence: 99%

Section: Observation Datamentioning

confidence: 99%

Section: Observation Datamentioning

confidence: 99%

Section: Observation Datamentioning

confidence: 99%

See 3 more Smart Citations

Evaluation of Rainfall Forecasts by Three Mesoscale Models during the Mei-yu Season of 2008 in Taiwan. Part II: Development of an Object-Oriented Method

2020

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Measured and modeled vertical structure of precipitation during mixed-phase event near the West Coast of the Antarctic Peninsula

Pishniak¹,

Razumnyi²

2022

UAJ

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Precipitation structures are easy to detect, however, the mesoscale atmospheric processes which they reflect are challenging to understand in Polar Regions and hard to model numerically. Currently, the spatial distribution of precipitation can be tracked at the resolution of minutes and seconds. For this purpose, the researchers at the Ukrainian Antarctic Akademik Vernadsky station employ several near-ground measurement systems and the Micro Rain Radar for remote vertical measurements. Measurements show stochastic precipitation variability caused by turbulence, precipitation bands related to the atmospheric processes of its formation, phase transition (melting) zones, and wind shears. The time scale of bands in the stratiform precipitation typically varied in the range of 5—15 minutes and corresponded to the 2—15 km spatial scale of atmospheric circulations according to the modeled parameters of the atmosphere. The Polar Weather Research and Forecast (Polar WRF) model was used to reveal the general atmospheric conditions. We also tested and evaluated its ability to reproduce small structures. A simple method based on typical model variables is proposed to identify the precipitation melting layer in the simulation data, similar to that determined by radars. The results were satisfyingly consistent with the position of the 0 °C isotherm in the model and with the radar measurements. In addition, the method highlighted supercooled mixed-phase precipitation. Modeling showed good results for large-scale processes like atmospheric fronts and general air mass features in the case study. However, even at the 1 km resolution the simulation reproduced thin mesoscale precipitation features smoothly, which sometimes looks unrealistic. As for other precipitation peculiarities, like band inclination, melting layer position, and mixed-phase zones, the Polar WRF model demonstrates high consistency with observations. The model can describe the atmospheric conditions except for the investigation of precipitation-initiating mechanisms, which still is a challenge for modeling at a small scale.

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Evaluation of Rainfall Forecasts by Three Mesoscale Models during the Mei-Yu Season of 2008 in Taiwan. Part III: Application of an Object-Oriented Verification Method

2020

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In this study, the performances of Mei-yu (May–June) quantitative precipitation forecasts (QPFs) in Taiwan by three mesoscale models: the Cloud-Resolving Storm Simulator (CReSS), the Central Weather Bureau (CWB) Weather Research and Forecasting (WRF), and the CWB Non-hydrostatic Forecast System (NFS) are explored and compared using an newly-developed object-oriented verification method, with particular focus on the various properties or attributes of rainfall objects identified. Against a merged dataset from ~400 rain gauges in Taiwan and the Tropical Rainfall Measuring Mission (TRMM) data in the 2008 season, the object-based analysis is carried out to complement the subjective analysis in a parallel study. The Mei-yu QPF skill is seen to vary with different aspects of rainfall objects among the three models. The CReSS model has a total rainfall production closest to the observation but a large number of smaller objects, resulting in more frequent and concentrated rainfall. In contrast, both WRF and NFS tend to under-forecast the number of objects and total rainfall, but with a higher proportion of bigger objects. Location errors inferred from object centroid locations appear in all three models, as CReSS, NFS, and WRF exhibit a tendency to simulate objects slightly south, east, and northwest with respect to the observation. Most rainfall objects are aligned close to an E–W direction in CReSS, in best agreement with the observation, but many towards the NE–SW direction in both WRF and NFS. For each model, the objects are matched with the observed ones, and the results of the matched pairs are also discussed. Overall, though preliminarily, the CReSS model, with a finer grid size, emerges as best performing model for Mei-yu QPFs.

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Evaluation of Rainfall Forecasts by Three Mesoscale Models during the Mei-yu Season of 2008 in Taiwan. Part I: Subjective Comparison

Cited by 3 publications

References 67 publications

Evaluation of Rainfall Forecasts by Three Mesoscale Models during the Mei-yu Season of 2008 in Taiwan. Part II: Development of an Object-Oriented Method

Evaluation of Rainfall Forecasts by Three Mesoscale Models during the Mei-yu Season of 2008 in Taiwan. Part II: Development of an Object-Oriented Method

Measured and modeled vertical structure of precipitation during mixed-phase event near the West Coast of the Antarctic Peninsula

Evaluation of Rainfall Forecasts by Three Mesoscale Models during the Mei-Yu Season of 2008 in Taiwan. Part III: Application of an Object-Oriented Verification Method

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