TY - JOUR
T1 - A method for probability matching based on the ensemble maximum for quantitative precipitation forecasts
AU - Qiao, Xiaoshi
AU - Wang, Shizhang
AU - Schwartz, Craig S.
AU - Liu, Zhiquan
AU - Min, Jinzhong
N1 - Publisher Copyright:
© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).
PY - 2020/8
Y1 - 2020/8
N2 - A probability matching (PM) product using the ensemble maximum (EnMax) as the basis for spatial reassignment was developed. This PM product was called the PM max and its localized version was called the local PM (LPM) max. Both products were generated from a 10-member ensemble with 3-km horizontal grid spacing and evaluated over 364 36-h forecasts in terms of the fractions skill score. Performances of the PM max and LPM max were compared to those of the traditional PM mean and LPM mean, which both used the ensemble mean (EnMean) as the basis for spatial reassignment. Compared to observations, the PM max typically outperformed the PM mean for precipitation rates $5 mm h21; this improvement was related to the EnMax, which had better spatial placement than the EnMean for heavy precipitation. However, the PM mean produced better forecasts than the PM max for lighter precipitation. It appears that the global reassignment used to produce the PM max was responsible for its poorer performance relative to the PM mean at light precipitation rates, as the LPM max was more skillful than the LPM mean at all thresholds. These results suggest promise for PM products based on the EnMax, especially for rare events and ensembles with insufficient spread.
AB - A probability matching (PM) product using the ensemble maximum (EnMax) as the basis for spatial reassignment was developed. This PM product was called the PM max and its localized version was called the local PM (LPM) max. Both products were generated from a 10-member ensemble with 3-km horizontal grid spacing and evaluated over 364 36-h forecasts in terms of the fractions skill score. Performances of the PM max and LPM max were compared to those of the traditional PM mean and LPM mean, which both used the ensemble mean (EnMean) as the basis for spatial reassignment. Compared to observations, the PM max typically outperformed the PM mean for precipitation rates $5 mm h21; this improvement was related to the EnMax, which had better spatial placement than the EnMean for heavy precipitation. However, the PM mean produced better forecasts than the PM max for lighter precipitation. It appears that the global reassignment used to produce the PM max was responsible for its poorer performance relative to the PM mean at light precipitation rates, as the LPM max was more skillful than the LPM mean at all thresholds. These results suggest promise for PM products based on the EnMax, especially for rare events and ensembles with insufficient spread.
UR - https://www.scopus.com/pages/publications/85091848063
U2 - 10.1175/MWR-D-20-0003.1
DO - 10.1175/MWR-D-20-0003.1
M3 - Article
AN - SCOPUS:85091848063
SN - 0027-0644
VL - 148
SP - 3379
EP - 3396
JO - Monthly Weather Review
JF - Monthly Weather Review
IS - 8
ER -