Mesoscale Resolution Radar Data Assimilation Experiments with the Harmonie Model

Serguei Ivanov,Igor Ruban,Silas Michaelides

doi:10.3390/rs10091453

Serguei Ivanov, Igor Ruban + Show 1 more

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https://doi.org/10.3390/rs10091453

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Abstract

This study presents a pre-processing approach adopted for the radar reflectivity data assimilation and results of simulations with the Harmonie numerical weather prediction model. The proposed method creates a 3D regular grid in which a horizontal size of meshes coincides with the horizontal model resolution. This minimizes the representative error associated with the discrepancy between resolutions of informational sources. After such preprocessing, horizontal structure functions and their gradients for radar reflectivity maintain the sizes and shapes of precipitation patterns similar to those of the original data. The method shows an improvement of precipitation prediction within the radar location area in both the rain rates and spatial pattern presentation. It redistributes precipitable water with smoothed values over the common domain since the control runs show, among several sub-domains with increased and decreased values, correspondingly. It also reproduces the mesoscale belts and cell patterns of sizes from a few to ten kilometers in precipitation fields. With the assimilation of radar data, the model simulates larger water content in the middle troposphere within the layer from 1 km to 6 km with major variations at 2.5 km to 3 km. It also reproduces the mesoscale belt and cell patterns of precipitation fields.

Highlights

Precipitation plays an important role in both the water cycle and energy balance of the atmosphere
The Global Precipitation Measurement (GPM) mission has shown notable differences in estimations of precipitation obtained from various platforms especially for low rain rates [1]
The objective of this study is to present a pre-processing approach adopted for the radar reflectivity data assimilation and the results of simulations with the Harmonie numerical weather prediction model

Summary

Introduction

Precipitation plays an important role in both the water cycle and energy balance of the atmosphere. Due to the high spatial and temporal variability of precipitation in the mesoscale, obtaining accurate quantitative precipitation estimates is still a “first-line frontier” task. Satellite-based infrared and visible data have high spatial resolution, relations between the radiance from the cloud and precipitation are indirect and non-unique [3]. Passive microwave instruments provide acceptable estimates of precipitation. They exhibit low spatial and temporal resolution [5]. The advantage of the ground-based radar network in Europe is the high spatiotemporal resolution. This is the reason to make a choice in favor of such data for predicting mesoscale precipitation patterns and associated atmospheric variables

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