Multimodel Ensemble Forecasts of Precipitation Based on an Object-Based Diagnostic Evaluation

Abstract

We analyzed 24-h accumulated precipitation forecasts over the 4-month period from 1 May to 31 August 2013 over an area located in East Asia covering the region 15.05°–58.95°N, 70.15°–139.95°E generated with the ensemble prediction systems (EPS) from ECMWF, NCEP, UKMO, JMA, and CMA contained in the TIGGE dataset. The forecasts are first evaluated with the Method for Object-Based Diagnostic Evaluation (MODE). Then a multimodel ensemble (MME) forecast technique that is based on weights derived from object-based scores is investigated and compared with the equally weighted MME and the traditional gridpoint-based MME forecast using weights derived from the point-to-point metric, mean absolute error (MAE). The object-based evaluation revealed that attributes of objects derived from the ensemble members of the five individual EPS forecasts and the observations differ consistently. For instance, their predicted centroid location is more southwestward, their shape is more circular, and their orientation is more meridional than in the observations. The sensitivity of the number of objects and their attributes to methodological parameters is also investigated. An MME prediction technique that is based on weights computed from the object-based scores, median of maximum interest, and object-based threat score is explored and the results are compared with the ensemble forecasts of the individual EPS, the equally weighted MME forecast, and the traditional superensemble forecast. When using MODE statistics for the forecast evaluation, the object-based MME prediction outperforms all other predictions. This is mainly because of a better prediction of the objects’ centroid locations. When using the precipitation-based fractions skill score, which is not used in either of the weighted MME forecasts, the object-based MME forecasts are slightly better than the equally weighted MME forecasts but are inferior to the traditional superensemble forecast that is based on weights derived from the point-to-point metric MAE.

Forecasts of the Warm-Sector Heavy Rainfall With a Warm Shear Pattern Over Coastal Areas of the Yangtze–Huaihe River in a Regional Business Forecast Model

“…Furthermore, the rainfall biases can be effectively eliminated by the advanced statistical model. For instance, object attributes in MODE describe the location and pattern of the rain belt, which could be applied for further development of advanced statistical model constructions to improve the prediction performances of WRWS events in the forecast models (Ji et al, 2020;Lyu et al, 2021;. The associated concerns are to be further studied in the future.…”

“…It allocates weight and confidence coefficients for predefined precipitation object attributes and calculates a total interest function based on a fuzzy logic approach, which quantifies the similarity between any two objects (Johnson and Wang, 2013). Four steps are involved in the MODE procedures, i.e., identifying objects, calculating object attributes, detecting matching objects between observations and predictions, and evaluating the similarity of the attributes, which are briefly introduced as follows and can be found in more detail in Ji et al (2020).…”

Intensive Cold-Air Invasion Detection and Classification with Deep Learning in Complicated Meteorological Systems

“…erefore, it has triggered a historic revolution in many fields [33,34]. In common meteorological research, low-temperature forecasting involves the combination of numerical prediction products and statistical theory [35][36][37]. e rise of artificial intelligence facilitates applying deep learning technology to the forecasting of meteorological elements and improving the accuracy of the forecast core research problems.…”

Diagnostic Analysis of Multimodel Rainstorm Forecast for Cases Based on MODE Method

“…Therefore, rainstorm research has always been the focus and hot issue of meteorological workers at home and abroad. The existing research work on rainstorm forecasts in the Qinling Mountains and surrounding areas of China mainly focuses on three aspects: first, based on weather situation [1][2][3][4], water-vapor transfer [5][6][7], small and medium-scale characteristics [8][9][10][11][12]; second, the numerical simulation of system structure and evolution process of rainstorm using a high-resolution regional numerical model [13,14]; third, the rainstorm The article mainly selects 7 rainstorm forecast cases in 2020. The specific rainstorm case information is shown in Table 1.…”