TY - JOUR
T1 - A prognostic nested k-nearest approach for microwave precipitation phase detection over snow cover
AU - Takbiri, Zeinab
AU - Ebtehaj, Ardeshir
AU - Foufoula-Georgiou, Efi
AU - Kirstetter, Pierre Emmanuel
AU - Turk, F. Joseph
N1 - Publisher Copyright:
© 2019 American Meteorological Society.
PY - 2019/2/1
Y1 - 2019/2/1
N2 - Monitoring changes of precipitation phase from space is important for understanding the mass balance of Earth's cryosphere in a changing climate. This paper examines a Bayesian nearest neighbor approach for prognostic detection of precipitation and its phase using passive microwave observations from the Global Precipitation Measurement (GPM) satellite. The method uses the weighted Euclidean distance metric to search through an a priori database populated with coincident GPM radiometer and radar observations as well as ancillary snow-cover data. The algorithm performance is evaluated using data from GPM official precipitation products, ground-based radars, and high-fidelity simulations from the Weather Research and Forecasting Model. Using the presented approach, we demonstrate that the hit probability of terrestrial precipitation detection can reach to 0.80, while the probability of false alarm remains below 0.11. The algorithm demonstrates higher skill in detecting snowfall than rainfall, on average by 10%. In particular, the probability of precipitation detection and its solid phase increases by 11% and 8%, over dry snow cover, when compared to other surface types. The main reason is found to be related to the ability of the algorithm in capturing the signal of increased liquid water content in snowy clouds over radiometrically cold snow-covered surfaces.
AB - Monitoring changes of precipitation phase from space is important for understanding the mass balance of Earth's cryosphere in a changing climate. This paper examines a Bayesian nearest neighbor approach for prognostic detection of precipitation and its phase using passive microwave observations from the Global Precipitation Measurement (GPM) satellite. The method uses the weighted Euclidean distance metric to search through an a priori database populated with coincident GPM radiometer and radar observations as well as ancillary snow-cover data. The algorithm performance is evaluated using data from GPM official precipitation products, ground-based radars, and high-fidelity simulations from the Weather Research and Forecasting Model. Using the presented approach, we demonstrate that the hit probability of terrestrial precipitation detection can reach to 0.80, while the probability of false alarm remains below 0.11. The algorithm demonstrates higher skill in detecting snowfall than rainfall, on average by 10%. In particular, the probability of precipitation detection and its solid phase increases by 11% and 8%, over dry snow cover, when compared to other surface types. The main reason is found to be related to the ability of the algorithm in capturing the signal of increased liquid water content in snowy clouds over radiometrically cold snow-covered surfaces.
KW - Algorithms
KW - Atmosphere
KW - Bayesian methods
KW - Data processing
KW - Remote sensing
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U2 - 10.1175/JHM-D-18-0021.1
DO - 10.1175/JHM-D-18-0021.1
M3 - Article
C2 - 31105470
AN - SCOPUS:85063590901
SN - 1525-755X
VL - 20
SP - 251
EP - 274
JO - Journal of Hydrometeorology
JF - Journal of Hydrometeorology
IS - 2
ER -