Abstract

A limited-area kilometre scale numerical weather prediction system is applied to evaluate the effect of refined surface data assimilation on short-range heavy precipitation forecasts. The refinements include a spatially dependent background error representation, use of a flow-dependent data assimilation technique, and use of data from a satellite-based scatterometer instrument. The effect of the enhancements on short-term prediction of intense precipitation events is confirmed through a number of case studies. Verification scores and subjective evaluation of one particular case points at a clear impact of the enhanced surface data assimilation on short-range heavy precipitation forecasts and suggest that it also tends to slightly improve them. Although this is not strictly statistically demonstrated, it is consistent with the expectation that a better surface state should improve rainfall forecasts.

Highlights

  • Numerical weather prediction (NWP) is concerned with, starting from an initial state, integrating the atmospheric state forward in time

  • The flow-dependent relations between observations and surface-soil state variables induced by the Kalman filter-based methodology is shown in Figure 7 and is compared to what is used in the reference system

  • This paper presents improved methodologies and enhanced observation usage for data assimilation with a km-scale limited area modelling system

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Summary

Background

Numerical weather prediction (NWP) is concerned with, starting from an initial state, integrating the atmospheric state forward in time. It was early realised that the forecast quality is strongly dependent on an accurate description of the initial state and, on the capability of the assimilation system [1] Surface processes, such as heat and water exchanges, are essential to represent in NWP. Our aim is to improve short-range NWP of extreme precipitation by enhancing the representation of the surface initial state by applying an improved HARMONIE-AROME surface data assimilation. The enhancements concern both methodologies in data assimilation and enhanced observation usage.

NWP System
Default Surface Data Assimilation of Temperature and Moisture
Modelling System Setup and Description of Cases
Overview
Screen-Level Structure Functions
Flow-Dependent Vertical Spreading of Information
Use of Remote Sensing Data
Organisation of Experiments
Results
Objective Verification
Subjective Verification of a Case Study
Conclusions
Full Text
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