scholarly journals Application of Contiguous Rain Area (CRA) Methods to Tropical Cyclone Rainfall Forecast Verification

2018 ◽  
Vol 5 (11) ◽  
pp. 736-752 ◽  
Author(s):  
Yingjun Chen ◽  
Elizabeth E. Ebert ◽  
Noel E. Davidson ◽  
Kevin J. E. Walsh
Keyword(s):  
Tropical Cyclone ◽  
Forecast Verification ◽  
Rain Area ◽  
Rainfall Forecast

scholarly journals NGFS rainfall forecast verification over India using the contiguous rain area (CRA) method

MAUSAM ◽  
2021 ◽  
Vol 66 (3) ◽  
pp. 415-422
Author(s):  
KULDEEP SHARMA ◽  
RAGHAVENDRA ASHRIT ◽  
GOPAL IYENGAR ◽  
ASHIS MITRA ◽  
ELIZABETH EBERT
Keyword(s):  
Forecast Verification ◽  
Rain Area ◽  
Rainfall Forecast

Toward Better Understanding of the Contiguous Rain Area (CRA) Method for Spatial Forecast Verification

2009 ◽  
Vol 24 (5) ◽  
pp. 1401-1415 ◽  
Author(s):  
Elizabeth E. Ebert ◽  
William A. Gallus
Keyword(s):  
Correlation Coefficient ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Weather Prediction ◽  
Forecast Errors ◽  
Forecast Verification ◽  
Rain Area ◽  
Squared Error ◽  
Verification Methods ◽  
The Mean

Abstract The contiguous rain area (CRA) method for spatial forecast verification is a features-based approach that evaluates the properties of forecast rain systems, namely, their location, size, intensity, and finescale pattern. It is one of many recently developed spatial verification approaches that are being evaluated as part of a Spatial Forecast Verification Methods Intercomparison Project. To better understand the strengths and weaknesses of the CRA method, it has been tested here on a set of idealized geometric and perturbed forecasts with known errors, as well as nine precipitation forecasts from three high-resolution numerical weather prediction models. The CRA method was able to identify the known errors for the geometric forecasts, but only after a modification was introduced to allow nonoverlapping forecast and observed features to be matched. For the perturbed cases in which a radar rain field was spatially translated and amplified to simulate forecast errors, the CRA method also reproduced the known errors except when a high-intensity threshold was used to define the CRA (≥10 mm h−1) and a large translation error was imposed (>200 km). The decomposition of total error into displacement, volume, and pattern components reflected the source of the error almost all of the time when a mean squared error formulation was used, but not necessarily when a correlation-based formulation was used. When applied to real forecasts, the CRA method gave similar results when either best-fit criteria, minimization of the mean squared error, or maximization of the correlation coefficient, was chosen for matching forecast and observed features. The diagnosed displacement error was somewhat sensitive to the choice of search distance. Of the many diagnostics produced by this method, the errors in the mean and peak rain rate between the forecast and observed features showed the best correspondence with subjective evaluations of the forecasts, while the spatial correlation coefficient (after matching) did not reflect the subjective judgments.

scholarly journals An Operational Statistical Scheme for Tropical Cyclone-Induced Rainfall Forecast

10.5772/64859 ◽  
2016 ◽  
Author(s):  
Qinglan Li ◽  
Hongping Lan ◽  
Johnny C.L. Chan ◽  
Chunyan Cao ◽  
Cheng Li ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Rainfall Forecast

scholarly journals An Alternative Tropical Cyclone Intensity Forecast Verification Technique

2008 ◽  
Vol 23 (6) ◽  
pp. 1304-1310 ◽  
Author(s):  
Sim D. Aberson
Keyword(s):  
Tropical Cyclone ◽  
New Technique ◽  
Lead Times ◽  
Tropical Cyclone Intensity ◽  
Slight Improvement ◽  
Forecast Verification ◽  
Cyclone Intensity ◽  
Skill Scores ◽  
A New Technique ◽  
National Hurricane Center

Abstract The National Hurricane Center (NHC) does not verify official or model forecasts if those forecasts call for a tropical cyclone to dissipate or if the real tropical cyclone dissipates. A new technique in which these forecasts are included in a contingency table with all other forecasts is presented. Skill scores and probabilities are calculated. Forecast verifications with the currently used technique have shown a slight improvement in intensity forecasts. The new technique, taking into account all forecasts, suggests that the probability of a forecast having a large (>30 kt) error is decreasing, and the likelihood of the error being less than about 10 kt is increasing in time, at all forecast lead times except 12 h when the forecasts are already quite good.

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