Along with scientific and technological developments, the advancement of the Global Observing System (GOS) has been one of the most important factors contributing to the increase in numerical weather forecasting (NWP) skill in recent years. The initial conditions of a forecast are provided by data assimilation systems, combining the latest short-range forecast with a selection of atmospheric observations. One of the current major limitations is the lack of global wind profile observations, particularly in regions and for spatial scales where geostrophic mass-wind coupling is weak. The European Space Agency’s (ESA) Doppler Wind Lidar (DWL) satellite mission Aeolus provides a novel data set of wind profiles with quasi-global coverage intended to fill these data gaps in the current GOS. Therefore, it is of great interest to assess the impact of the Aeolus observations in NWP to demonstrate the potential value of such satellite-based DWL missions.
A crucial prerequisite for using meteorological observations in NWP data assimilation systems is a comprehensive knowledge of their errors. The first part of this thesis investigates the Aeolus data quality through comparisons with three independent reference data sets: collocated radiosonde observations and model equivalents of the global ICOsahedral Nonhydrostatic (ICON) model of Deutscher Wetterdienst (DWD) and the Integrated Forecast System (IFS) model of the European Centre for Medium-Range Weather Forecast (ECMWF).
This enables a comprehensive estimation and characterization of the systematic and random errors of the Aeolus wind profile observations. Approaches to correct for the analyzed complex systematic errors that can be used in the context of quality control in data assimilation systems are being tested. Furthermore, to obtain a meaningful estimation of the Aeolus instrumental error, the representativeness errors for the comparisons are determined based on high-resolution regional model simulations. The results show the importance of accounting for representativeness errors for the mission’s calibration and validation activities and provide an estimate of the vertical structure of the Aeolus Rayleigh and Mie wind instrumental error that can be used for the assigned observation error in data assimilation.
The second part of this thesis examines how numerical weather forecasting benefits from the assimilation of the novel DWL observations from the Aeolus satellite.
Table of Contents
1 Introduction
1.1 State of the art
1.1.1 Importance of atmospheric wind observations for NWP
1.1.2 Deficiencies in the global observing system
1.1.3 Wind profile observations from space - historical scientific and technological background
1.1.4 Aeolus mission objectives
1.2 Research goals and outline of the thesis
2 Basic principles
2.1 Aeolus observations
2.1.1 DWL ALADIN measurement concept
2.1.2 Level-2B wind processing
2.1.3 Overview: Satellite orbit and measurement geometry
2.1.4 Main changes in the Aeolus L2B data set during the mission
2.2 Data assimilation
2.2.1 Three-dimensional variational assimilation
2.2.2 Ensemble Kalman Filter
2.2.3 Global data assimilation system at DWD
2.2.4 Observation error components in data assimilation
3 Data and methodology
3.1 Data sets and methods for the Aeolus HLOS wind validation study
3.1.1 Evaluation period and region
3.1.2 Collocated radiosonde observations
3.1.3 DWD ICON and ECMWF IFS model equivalents
3.1.4 Quality control criteria for the Aeolus HLOS wind observations
3.1.5 Representativeness errors
3.1.6 Statistical metrics
3.2 Data sets and methods to assess the impact of Aeolus HLOS wind assimilation
3.2.1 Experimental set up
3.2.2 Verification data and methods
4 Results: Validation of Aeolus HLOS wind observations
4.1 Temporal evolution of systematic and random differences
4.2 Aeolus HLOS wind validation error estimates
4.2.1 Representativeness error
4.2.2 Model error and radiosonde wind observational error
4.2.3 Aeolus wind observational error
4.2.4 Comparison and classification of the validation error estimates
4.3 Rayleigh wind observation bias dependencies and correction approaches
4.3.1 Rayleigh wind bias dependence on latitude and orbit phase
4.3.2 Rayleigh wind bias dependence on latitude, longitude and orbit phase
4.4 Concluding remarks on the validation of Aeolus HLOS wind observations
5 Results: Impact of Aeolus HLOS wind assimilation in the global model ICON
5.1 A global statistical overview
5.1.1 Systematic changes in the analysis
5.1.2 Short-range forecast impact: Observation-based verification
5.1.3 Medium-range forecast impact: Analysis-based verification
5.2 Investigation of links between dynamical scenarios and particularly high impact of Aeolus on NWP forecasts
5.2.1 Impact on tropical stratospheric wind variations (QBO)
5.2.2 Impact on change in the ENSO state in the Eastern Pacific
5.2.3 Dynamical impact in the midlatitudes
5.3 Concluding remarks on the impact of Aeolus HLOS wind assimilation in the global model ICON
6 Conclusions and Outlook
6.1 Main conclusions
6.2 Outlook
Research Objectives and Themes
This thesis investigates the quality of Aeolus satellite wind observations and evaluates their impact on Numerical Weather Prediction (NWP) using the ICON data assimilation system. The primary research questions concern the characterization and correction of systematic errors in Aeolus data and the quantification of the relative benefit of these observations in global assimilation systems, particularly in relation to tropical and extratropical dynamical scenarios.
- Validation of Aeolus wind profile data against independent reference datasets.
- Estimation of observational and representativeness errors for data assimilation.
- Development and testing of bias correction approaches for Rayleigh wind observations.
- Analysis of forecast improvements from Aeolus data assimilation using Observing System Experiments (OSEs).
- Investigation of the dynamical influence of Aeolus on large-scale circulation systems like QBO, ENSO, and extratropical transition events.
Excerpt from the Book
1.1.1 Importance of atmospheric wind observations for NWP
Along with temperature, pressure, and humidity, wind is one of the fundamental variables that describe the physical state of the atmosphere. A familiar characteristic of winds is that they change from hour to hour, from day to day, and from place to place. This variability on a wide range of temporal and spatial scales associated with weather systems, storms, or fronts is of great importance for meteorological forecasting (Goody and Walker, 1972). The average overall structure of large-scale atmospheric motion is usually defined as the general circulation. It results from the differential solar heating between the equatorial regions and the poles in conjunction with the Earth’s rotation. Due to mutual interactions, the general circulation is closely related to variable winds on smaller scales. Variations in small-scale atmospheric phenomena can significantly change the average flow pattern, mainly associated with the evolution of atmospheric waves. On the other hand, there are a variety of stationary or seasonal wind systems on Earth that can affect the small-scale weather pattern. In the following, an overview of the main wind-driven dynamical processes in the atmosphere is first given to illustrate the general importance of wind observations. Then, the role and need for direct wind field information in Numerical weather prediction (NWP) are discussed.
Atmospheric wind-driven dynamical processes
In the tropics, seasonally varying monsoon circulations play an important role in the annual evolution of precipitation and temperature patterns. The major monsoon systems around the equator are the Indian, East Asian, and West African Monsoons, which are associated with the latitudinal variation of the Intertropical Convergence Zone (ITCZ). Their variability may be significantly related to jet streams such as the Tropical Easterly Jet (TEJ) in the upper troposphere and the mid-level African Easterly Jet (AEJ). Furthermore, one of the main sources of tropical predictability on the weekly to monthly time scale is the intraseasonal wave-like convective system Madden-Julian Oscillation (MJO). Besides the tropics, the MJO can also impact the weather in the extratropics by inducing Rossby waves. Another very central teleconnection system on the interannual time scale is the El Niño-Southern Oscillation (ENSO). It manifests as changes in the east-
Summary of Chapters
1 Introduction: Provides an overview of the role of wind observations in NWP, details the deficiencies in current observing systems, and outlines the research goals regarding the Aeolus mission.
2 Basic principles: Covers the physical fundamentals of Doppler Wind Lidars (DWL), the Aeolus ALADIN instrument design, and the theoretical framework of data assimilation methods relevant to this study.
3 Data and methodology: Describes the reference data sets used for validation, the quality control criteria applied to Aeolus data, and the setup of the Observing System Experiments (OSE) performed with the ICON model.
4 Results: Validation of Aeolus HLOS wind observations: Details the validation results for the systematic and random errors of Aeolus data, including the estimation of representativeness errors and various bias correction techniques.
5 Results: Impact of Aeolus HLOS wind assimilation in the global model ICON: Analyzes the impact of assimilating Aeolus data on analysis and forecast performance, with a specific focus on its influence during key dynamical scenarios like QBO, ENSO, and extratropical transition.
6 Conclusions and Outlook: Summarizes the main findings of the thesis and offers perspectives on future research and potential follow-on satellite missions.
Keywords
Aeolus, Doppler Wind Lidar, Data Assimilation, Numerical Weather Prediction, ICON, Validation, Systematic Errors, QBO, ENSO, Extratropical Transition, Representativeness Error, Rayleigh-Brillouin scattering, Observation Impact, Atmospheric Dynamics, Satellite Meteorology.
Frequently Asked Questions
What is the primary focus of this dissertation?
The dissertation focuses on the validation of Aeolus Doppler Wind Lidar (DWL) observations and the evaluation of their impact on numerical weather prediction within the ICON global model.
Which specific observation types does this study analyze?
The study analyzes Aeolus HLOS (Horizontal Line-of-Sight) wind observations, specifically separating them into Rayleigh-clear and Mie-cloudy wind products.
What is the goal of the validation part of this research?
The validation aims to characterize the systematic and random errors of Aeolus observations, determine the Aeolus observational error, and develop bias correction approaches.
How is the impact of Aeolus wind data assessed in the global model?
The impact is assessed through Observing System Experiments (OSEs), comparing a control run (without Aeolus) with an experimental run (with Aeolus) over a three-month period.
Which main dynamical systems are highlighted as areas of high impact?
The study highlights the impact of Aeolus observations on large-scale circulation systems, specifically the Quasi-Biennial Oscillation (QBO), the El Niño-Southern Oscillation (ENSO), and the extratropical transition of tropical cyclones.
What are the key statistical metrics used for validation?
The validation uses bias and random difference estimates, specifically employing the scaled Median Absolute Deviation (MAD) as a robust indicator of variance.
How does the representativeness error influence the study results?
Representativeness error arises due to the difference between line-based satellite measurements and model point-based representations, making it a critical component for correctly assigning observation error in data assimilation.
What is the conclusion regarding the benefit of Aeolus observations for NWP?
The research concludes that Aeolus provides a beneficial impact on NWP forecasts, particularly in data-sparse regions like the tropics and the Southern Hemisphere, and contributes significantly to the understanding of large-scale dynamical shifts.
- Arbeit zitieren
- Anonym (Autor:in), 2023, Validation and Assimilation of Aeolus Wind Observations, München, GRIN Verlag, https://www.grin.com/document/1391403
