Conference programme

Back to the programme printer.gif Print




Delegates are invited to meet and discuss with the poster presenters during the poster presentation sessions between 10:30-11:30 and 16:00-17:00 on Thursday, 19 November 2015.

Lead Session Chair:
Stephan Barth, ForWind - Center for Wind Energy Research, Germany
Gil Lizcano Vortex, Spain
Co-authors:
Gil Lizcano (1) F
(1) Vortex, Barcelona, Spain

Share this poster on:

Printer friendly version: printer.gif Print

Presenter's biography

Biographies are supplied directly by presenters at EWEA 2015 and are published here unedited

Gil Lizcano has more that 15 years working on the climate and wind meteorology sector in both academia and private industry where he gained a combined experience in climate analysis and mesoscale wind modeling.
In 2008, he joined Vortex and leads the company R&D efforts. He is also in charge of bringing climate knowledge for the modeling lines implemented by Vortex
Previously, he worked for six years at Environmental Change Institute at the University of Oxford, participating in different international projects dealing with climate change impact assessment.
He also spent over five year at the Brazilian Wind Energy Center involved in different regional mapping initiatives. He also worked for Nordex as wind analyst.


Poster

Poster Download poster (7.30 MB)

Abstract

New methods to investigate modeled wind time series quality

Introduction

Reanalysis and downscaled products provide retrospective climate data that span over several decades which allow long-term adjustments of wind conditions. A wide range of observational data sources were required to prescribe the ancillary inputs during the Reanalysis construction. These data have been upgraded according to different technology advances. Therefore, a large effort was required to homogenize and to calibrate the observations over time before being assimilated in the production. Whether the data massaging and homogenization prevented the Reanalysis products to be affected by artificial jumps and trends is an active research question.


Approach

Structural changes and trends of Reanalysis projects data (MERRA, CFS and ERA-Interim) and derived WRF downscaled wind time series were inspected and mapped using robust statistical tests. Analysis of the Reanalysis ancillary observational data (sounding, scatterometer and metstations) were carried out to complete the exploratory analysis and to infer conclusions about the control of the observations on the detected structural changes and trends.
Link to interannual variability index were portrayed to assess low frequency variability control of the time series changes. Analysis of the attenuation of inhomogeneities in downscaled products is also shown.
MERRA Gridded Innovation dataset were employed to inspect observational layer behind the reanalysis project.


Main body of abstract

This works shows results of a comprehensive analysis of consistency issues in latest Reanalysis projects with a wind resource calibration perspective. Methods to link changes in Reanalysis observational availability and model bias were employed to better understand suspicious trends and changes. A climate quality methodology is proposed to test Reanalysis and derived mesoscale products data against inhomogeneities and to provide some elements to discern natural and artificial variability changes and trends .


Conclusion

Characterization of uncertainty in Reanalysis and Mesoscale time series is a frequent request by end-users. Intercomparison with observation is a common practice for ascerting realism of modeled data. But measures of uncertainty are seldom provided. Time consistency of modeled time series is a fundamental stage when addressing uncertainty of model data.
This work proposes a methodology to assess consistency across modern generation of Reanalysis and derived products. Results from a global Reanalysis consistency mapping and guidelines to assess artificial control by Reanalysis observations and natural climate variability are presented to support the decision on the quality of modeled times series.


Learning objectives
Upon completions, delegates will:
- get an insight on uncertainty of modeled data over time;
- get a critical understanding of consistency issues found across the Reanalysis projects;
- be familiar with methods and data to inspect the artificial and natural drivers of changes found in wind conditions modeled time series;
- reconsider the assumption that "weird does necessary mean non-valid" when inspecting modeled time series;