Monthly Precipitation Forecast of Jiulong River Basin Based on Association Rule Analysis

2021 ◽  
Author(s):  
Linjiang Nan ◽  
Mingxiang Yang ◽  
Jianqiu Li ◽  
Ningpeng Dong ◽  
Hejia Wang
Keyword(s):  
River Basin ◽  
Association Rule ◽  
Precipitation Forecast ◽  
Monthly Precipitation ◽  
Jiulong River ◽  
Rule Analysis

Mining Co-Occurrence Patterns among Deep Road Distresses Using Association Rule Analysis

2022 ◽  
Vol 148 (1) ◽  
Author(s):  
Qian Gao ◽  
Chenglong Liu ◽  
Yishun Li ◽  
Yuchuan Du ◽  
Guanghua Yue ◽  
...  
Keyword(s):  
Association Rule ◽  
Rule Analysis ◽  
Occurrence Patterns

scholarly journals Forecasts covering one month using a cut-cell model

2013 ◽  
Vol 6 (4) ◽  
pp. 875-882 ◽  
Author(s):  
J. Steppeler ◽  
S.-H. Park ◽  
A. Dobler
Keyword(s):  
Cell Model ◽  
Control Model ◽  
Precipitation Forecast ◽  
Target Area ◽  
Strong Impact ◽  
Monthly Precipitation ◽  
Small Areas ◽  
Cut Cell ◽  
Terrain Following ◽  
The Impact

Abstract. This paper investigates the impact and potential use of the cut-cell vertical discretisation for forecasts covering five days and climate simulations. A first indication of the usefulness of this new method is obtained by a set of five-day forecasts, covering January 1989 with six forecasts. The model area was chosen to include much of Asia, the Himalayas and Australia. The cut-cell model LMZ (Lokal Modell with z-coordinates) provides a much more accurate representation of mountains on model forecasts than the terrain-following coordinate used for comparison. Therefore we are in particular interested in potential forecast improvements in the target area downwind of the Himalayas, over southeastern China, Korea and Japan. The LMZ has previously been tested extensively for one-day forecasts on a European area. Following indications of a reduced temperature error for the short forecasts, this paper investigates the model error for five days in an area influenced by strong orography. The forecasts indicated a strong impact of the cut-cell discretisation on forecast quality. The cut-cell model is available only for an older (2003) version of the model LM (Lokal Modell). It was compared using a control model differing by the use of the terrain-following coordinate only. The cut-cell model improved the precipitation forecasts of this old control model everywhere by a large margin. An improved, more transferable version of the terrain-following model LM has been developed since then under the name CLM (Climate version of the Lokal Modell). The CLM has been used and tested in all climates, while the LM was used for small areas in higher latitudes. The precipitation forecasts of the cut-cell model were compared also to the CLM. As the cut-cell model LMZ did not incorporate the developments for CLM since 2003, the precipitation forecast of the CLM was not improved in all aspects. However, for the target area downstream of the Himalayas, the cut-cell model considerably improved the prediction of the monthly precipitation forecast even in comparison with the modern CLM version. The cut-cell discretisation seems to improve in particular the localisation of precipitation, while the improvements leading from LM to CLM had a positive effect mainly on amplitude.

scholarly journals Global Evaluation of Seasonal Precipitation and Temperature Forecasts from NMME

2020 ◽  
Vol 21 (11) ◽  
pp. 2473-2486
Author(s):  
Tirthankar Roy ◽  
Xiaogang He ◽  
Peirong Lin ◽  
Hylke E. Beck ◽  
Christopher Castro ◽  
...  
Keyword(s):  
Phase 1 ◽  
Research Unit ◽  
Forecast Skill ◽  
Precipitation Forecast ◽  
Spatiotemporal Variability ◽  
Lead Times ◽  
Global Evaluation ◽  
Monthly Precipitation ◽  
Tropical Regions ◽  
Precipitation And Temperature

AbstractWe present a comprehensive global evaluation of monthly precipitation and temperature forecasts from 16 seasonal forecasting models within the NMME Phase-1 system, using Multi-Source Weighted-Ensemble Precipitation version 2 (MSWEP-V2; precipitation) and Climate Research Unit TS4.01 (CRU-TS4.01; temperature) data as reference. We first assessed the forecast skill for lead times of 1–8 months using Kling–Gupta efficiency (KGE), an objective performance metric combining correlation, bias, and variability. Next, we carried out an empirical orthogonal function (EOF) analysis to compare the spatiotemporal variability structures of the forecasts. We found that, in most cases, precipitation skill was highest during the first lead time (i.e., forecast in the month of initialization) and rapidly dropped thereafter, while temperature skill was much higher overall and better retained at higher lead times, which is indicative of stronger temporal persistence. Based on a comprehensive assessment over 21 regions and four seasons, we found that the skill showed strong regional and seasonal dependencies. Some tropical regions, such as the Amazon and Southeast Asia, showed high skill even at longer lead times for both precipitation and temperature. Rainy seasons were generally associated with high precipitation skill, while during winter, temperature skill was low. Overall, precipitation forecast skill was highest for the NASA, NCEP, CMC, and GFDL models, and for temperature, the NASA, CFSv2, COLA, and CMC models performed the best. The spatiotemporal variability structures were better captured for precipitation than temperature. The simple forecast averaging did not produce noticeably better results, emphasizing the need for more advanced weight-based averaging schemes.

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