Towards assimilation of InSAR data in operational weather models

Gert Mulder; Freek van Leijen; Jan Barkmeijer; Siebren de Haan; Ramon Hanssen

Towards assimilation of InSAR data in operational weather models

Gert Mulder, Freek van Leijen, Jan Barkmeijer, Siebren de Haan, Ramon Hanssen

Mathematical Geodesy and Positioning

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Abstract

InSAR signal delays due to the varying atmospheric refractivity are a potential data source to improve weathermodels [1]. Especially with the launch of the new Sentinel-1 satellites, which increases data coverage, latency andaccessibility, it may become possible to operationalize the assimilation of differential integrated refractivity (DIR)values in numerical weather models. Although studies exist on comparison between InSAR data and weathermodels [2], the impact of assimilation of DIR values in an operational weather model has never been assessed. Inthis study we present different ways to assimilate DIR values in an operational weather model and show the firstforecast results.

Original language	English
Number of pages	1
Publication status	Published - 2017
Event	EGU General Assembly 2017 - Vienna, Austria Duration: 23 Apr 2017 → 28 Apr 2017 http://www.egu2017.eu/

Conference

Conference	EGU General Assembly 2017
Abbreviated title	EGU 2017
Country/Territory	Austria
City	Vienna
Period	23/04/17 → 28/04/17
Internet address	http://www.egu2017.eu/

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EGU2017-14387Final published version, 35.4 KBLicence: CC BY

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title = "Towards assimilation of InSAR data in operational weather models",

abstract = "InSAR signal delays due to the varying atmospheric refractivity are a potential data source to improve weathermodels [1]. Especially with the launch of the new Sentinel-1 satellites, which increases data coverage, latency andaccessibility, it may become possible to operationalize the assimilation of differential integrated refractivity (DIR)values in numerical weather models. Although studies exist on comparison between InSAR data and weathermodels [2], the impact of assimilation of DIR values in an operational weather model has never been assessed. Inthis study we present different ways to assimilate DIR values in an operational weather model and show the firstforecast results.",

author = "Gert Mulder and {van Leijen}, Freek and Jan Barkmeijer and {de Haan}, Siebren and Ramon Hanssen",

year = "2017",

language = "English",

note = "EGU General Assembly 2017, EGU 2017 ; Conference date: 23-04-2017 Through 28-04-2017",

url = "http://www.egu2017.eu/",

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TY - CONF

T1 - Towards assimilation of InSAR data in operational weather models

AU - Mulder, Gert

AU - van Leijen, Freek

AU - Barkmeijer, Jan

AU - de Haan, Siebren

AU - Hanssen, Ramon

PY - 2017

Y1 - 2017

N2 - InSAR signal delays due to the varying atmospheric refractivity are a potential data source to improve weathermodels [1]. Especially with the launch of the new Sentinel-1 satellites, which increases data coverage, latency andaccessibility, it may become possible to operationalize the assimilation of differential integrated refractivity (DIR)values in numerical weather models. Although studies exist on comparison between InSAR data and weathermodels [2], the impact of assimilation of DIR values in an operational weather model has never been assessed. Inthis study we present different ways to assimilate DIR values in an operational weather model and show the firstforecast results.

AB - InSAR signal delays due to the varying atmospheric refractivity are a potential data source to improve weathermodels [1]. Especially with the launch of the new Sentinel-1 satellites, which increases data coverage, latency andaccessibility, it may become possible to operationalize the assimilation of differential integrated refractivity (DIR)values in numerical weather models. Although studies exist on comparison between InSAR data and weathermodels [2], the impact of assimilation of DIR values in an operational weather model has never been assessed. Inthis study we present different ways to assimilate DIR values in an operational weather model and show the firstforecast results.

M3 - Abstract

T2 - EGU General Assembly 2017

Y2 - 23 April 2017 through 28 April 2017

ER -

Towards assimilation of InSAR data in operational weather models

Abstract

Conference

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