The Integrated Nowcasting through Comprehensive Analysis (INCA) System and Its Validation over the Eastern Alpine Region

TLDR

The paper introduces the INCA system, a nowcasting framework designed for mountainous regions. INCA merges station and remote‑sensing observations to generate detailed analyses of meteorological fields, applies correlation‑based motion vectors with an intensity‑dependent elevation effect for short‑term nowcasts, and blends these into NWP forecasts using a temporal weighting function, with cross‑validation for verification. The system achieves high temperature analysis accuracy but lower wind and precipitation accuracy, provides significant short‑term improvement over NWP, and continues to enhance temperature forecasts at longer lead times despite diminishing observation influence.

Abstract

Abstract This paper presents the Integrated Nowcasting through Comprehensive Analysis (INCA) system, which has been developed for use in mountainous terrain. Analysis and nowcasting fields include temperature, humidity, wind, precipitation amount, precipitation type, cloudiness, and global radiation. The analysis part of the system combines surface station data with remote sensing data in such a way that the observations at the station locations are reproduced, whereas the remote sensing data provide the spatial structure for the interpolation. The nowcasting part employs classical correlation-based motion vectors derived from previous consecutive analyses. In the case of precipitation the nowcast includes an intensity-dependent elevation effect. After 2–6 h of forecast time the nowcast is merged into an NWP forecast provided by a limited-area model, using a predefined temporal weighting function. Cross validation of the analysis and verification of the nowcast are performed. Analysis quality is high for temperature, but comparatively low for wind and precipitation, because of the limited representativeness of station data in mountainous terrain, which can be only partially compensated by the analysis algorithm. Significant added value of the system compared to the NWP forecast is found in the first few hours of the nowcast. At longer lead times the effects of the latest observations becomes small, but in the case of temperature the downscaling of the NWP forecast within the INCA system continues to provide some improvement compared to the direct NWP output.