Abstract
Radar-based precipitation nowcasting refers to predicting rain for a
short period of time using radar reflectivity images. For dynamic
nowcasting, motion fields can be extrapolated using an approximate and
localized reduced-order model. Motion field estimation based on
traditional Horn-Schunck (HS) algorithm suffers from over-smoothing at
discontinuities in non-rigid and dissolving texture present in
precipitation nowcasting products. An attempt to preserve the
discontinuities using an L1 norm formulation in HS led to the use of
Total Variation L1 norm (TVL1). In this paper, we propose a radar-based
precipitation nowcasting model with TVL1-based estimation of motion
field and Sparse Identification of Non-linear Dynamics (SINDy)-based
estimation of non-linear dynamics. TVL1 is effective in preserving the
edges especially in the case of the eye of typhoons and squall lines
while estimating motion vectors. SINDy captures the non-linear dynamics
and generates the subsequent update values for the motion field based on
a reduced-order representation. Finally, the SINDy-generated ensemble of
motion field is used along with
the radar reflectivity image for generating precipitation nowcasts. We
evaluated the effectiveness of TVL1 in preserving edges while capturing
the motion field from non-rigid surfaces. The performance of the
proposed TVL1-SINDy model in nowcasting weather events such as Typhoons
and Squall lines are evaluated using performance metrics such as Mean
Absolute Error (MAE), and Critical Success Index (CSI). Experimental
results show that the proposed nowcasting system demonstrates better
performance compared to the benchmark nowcasting models with lower MAE,
higher CSI at higher lead times.