TY - JOUR
T1 - Assessment of deterministic and probabilistic precipitation nowcasting techniques over New York metropolitan area
AU - Tounsi, Achraf
AU - Temimi, Marouane
AU - Abdelkader, Mohamed
AU - Gourley, Jonathan J.
N1 - Publisher Copyright: © 2023 Elsevier Ltd
PY - 2023/10
Y1 - 2023/10
N2 - This study evaluates spatiotemporal variability of deterministic and probabilistic precipitation nowcasting models' performance over the greater New York City area. Five deterministic (LINDA-D, ANVIL, S-PROG, RM-DR, RM-S) and two probabilistic (LINDA-P and STEPS) nowcasting methods were assessed using Multi-Radar Multi-Sensor (MRMS) data from 2014 to 2022. Three lead times of 2, 4 and 6 h were considered. LINDA-P had the best average Pearson's correlation of 0.87 at the first step and 0.47 at the last one and the longest 80 min average decorrelation times. Its Mean Absolute Error (MAE) was four times lower in winter than in summer, 0.3 mm vs. 1.2 mm, respectively. LINDA-P scored better than other Lagrangian Persistence-based approaches for moderate and heavy rainfall. Overall, LINDA algorithms outperformed the other nowcasting methods, but at twice the average runtime cost compared to STEPS. Uncertainties can be attributed, among others, to the models' capability to simulate the dissipation and growth processes.
AB - This study evaluates spatiotemporal variability of deterministic and probabilistic precipitation nowcasting models' performance over the greater New York City area. Five deterministic (LINDA-D, ANVIL, S-PROG, RM-DR, RM-S) and two probabilistic (LINDA-P and STEPS) nowcasting methods were assessed using Multi-Radar Multi-Sensor (MRMS) data from 2014 to 2022. Three lead times of 2, 4 and 6 h were considered. LINDA-P had the best average Pearson's correlation of 0.87 at the first step and 0.47 at the last one and the longest 80 min average decorrelation times. Its Mean Absolute Error (MAE) was four times lower in winter than in summer, 0.3 mm vs. 1.2 mm, respectively. LINDA-P scored better than other Lagrangian Persistence-based approaches for moderate and heavy rainfall. Overall, LINDA algorithms outperformed the other nowcasting methods, but at twice the average runtime cost compared to STEPS. Uncertainties can be attributed, among others, to the models' capability to simulate the dissipation and growth processes.
KW - Heavy rainfall
KW - MRMS
KW - Nowcasting
KW - Precipitation
KW - Weather prediction
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U2 - 10.1016/j.envsoft.2023.105803
DO - 10.1016/j.envsoft.2023.105803
M3 - Article
SN - 1364-8152
VL - 168
JO - Environmental Modelling and Software
JF - Environmental Modelling and Software
M1 - 105803
ER -