using satellite data to understand uncertainties in reanalyses: uerra richard renshaw, peter jermey...
TRANSCRIPT
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Using satellite data to understand uncertainties in reanalyses: UERRA
Richard Renshaw, Peter Jermey
with thanks to Jörg Trentmann, Jennifer Lenhardt, Andrea Kaiser-Weiss (DWD)
© Crown Copyright 2012 Source: Met Office
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Outline
1. Why Regional Reanalysis ?
2. EURO4M Regional Reanalysis – Evaluation
3. Regional Reanalysis Plans
• Uncertainty Estimation in Regional ReAnalysis (UERRA) project
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What is a reanalysis ?
state-of-the-art NWP
past observations
gridded analyses
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• Gridded data• Based on observations• Incorporates model equations• Physically and dynamically coherent• Full set of meteorological fields• We can estimate accuracy
Why would anyone want a reanalysis ?
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Global Reanalyses
• NCEP-NCAR (1995) 250km• ECMWF ERA-40 (2004) 130km• ECMWF ERA-Interim (2010) 80km• NOAA/CIRES 20th C (2011) 200km• JMA JRA-55 (2013) 60km• ...
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ERA Interim ReanalysisDee et al, 2011
• Global atmosphere, T255 (80km), 60 vertical levels• 12-hour 4D-Var• 1979 - present
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© Crown copyright Met Office
1. Why Regional Reanalysis ?
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Evidence from operational NWP
25km Global
vs
12km NAE
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...the benefits of resolution
forecast range
screen temperature rms error (K)
global 25km
NAE 12km
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...and the disadvantage of boundaries!
forecast range
mean sea level pressure
rms error (Pa)
global
NAE
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• EU-project, April 2010 – March 2014, 9 partners
• Goal: LONG-TERM CLIMATE DATASETS + ASSESSMENTS OF CHANGE …describing climate variability and change at the European scale
…placing high-impact extreme events in a historical context
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European Regional Reanalysis: EURO4M project
• EURO4M project (2010-2014) developed UM regional reanalysis, tested on 2 year period (2008-2009).
• Resolution: 12km model, 24km 4D-Var• Lateral boundary conditions from ERA-Interim• ECMWF observation archive
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Increase in resolution
EURO4M: Model/DA: 12/24kmERA-Interim: Model/DA 80/125km
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Observations• Surface (SYNOP, buoy, etc)• Upper air (sonde, pilot, wind profiler)• Aircraft• AMV (‘satwinds’)• GPS-RO and ground-based GPS• Scatterometer winds• ATOVS• AIRS• IASI• MSG clear sky radiances
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Getting more from surface obs...
• Visibility• Cloud• Rainfall
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© Crown copyright Met Office
2. EURO4M Regional ReanalysisEvaluation
Peter Jermey
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Russian heatwave, July 2010
Tmax, 10-07-2010
e-obs
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www.ecad.eu
KNMI, Ge Verver et aI
ECAD: European Climate Assessment and Dataset
Daily data from 1950 -
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Maximum temperature on 10 July 2010 (during the Russian heat wave)
obs daily 25km grid
“E-Obs”
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Tmax10-07-10
ERA-Interim
12km EURO4M
obs
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© Crown copyright Met Office
Climate Statistics
Monthly Means
MO
ERA T
Compare with ECA&D statistics from obs stations
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© Crown copyright Met Office
Precipitation
Higher resolution should lead to improved representation of extremes
Covers wide range of intensities, periods and scales
Flooding in central Europe in 2013 caused 25 deaths and 12bn Euros damage
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© Crown copyright Met Office
Floods July 2008
9000 houses damaged
20,000ha ag. land flooded
300 houses destroyed
7500ha ag. land flooded
50,000 houses flooded
cost $700million
5 dead
$100 million
300,000 people affected
38 dead 3 dead
$300million
ROMANIA MOLDOVA UKRAINE
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© Crown copyright Met Office
Floods July 200823-26th July
Accumulations
SYNOP ERA-Interim UKMO
15mmMean abs error
13mm
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© Crown copyright Met Office
ETS precip scores
6hr
ERA-Interim
HIRLAM
Met Office
Truth is SYNOP rain gauge data
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© Crown copyright Met Office
Frequency bias
6hr
At low thresholds models over-represent
At high thresholds models under-represent, but …
… bias is reduced by increased resolution & 4DVAR assimilation
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Concept, methods and results
Deutscher Wetterdienst (DWD)
Jörg Trentmann, Jennifer Lenhardt
Evaluation of EURO4M Reanalysis data using Satellite Data
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Data sets (monthly means)
28
EURO4M Reanalyses vs. CM SAF
EURO4M Final Assembly – 03/2014
CM SAF products
→ CLARA-A1 (AVHRR Cloud Cover at 0.25°)
→ ATOVS (Integrated Water Vapour at 90x90 km)
EURO4M product
→ Merged GPCC (raingauge, land)/HOAPS (SSM/I, ocean) (Precipitation at 0.5°)
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Mean differences, cloud cover,MetOffice - CM SAF (AVHRR), July 2008/9
29EURO4M Final Assembly – 03/2014
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Mean differences, precipitation,MetOffice - CM SAF, July 2008
30EURO4M Final Assembly – 03/2014
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Mean differences, water vapour,MetOffice - CM SAF (ATOVS), July 2008/9
31EURO4M Final Assembly – 03/2014
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Validation• Reanalysis is only useful if we know the errors• Reanalysis fields are already of good quality• Conventional obs have limited coverage• Validation datasets need to be independent• Datasets need to be good quality, with error
estimates• Some variables difficult to validate
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© Crown copyright Met Office
Regional Ensemble
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Uncertainties from ensembles
Calibrate for variables we can validate
Get uncertainties for variables we can’t
validate
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Uncertainty Estimation in Regional ReAnalysis (UERRA) Project
• EURO4M represents just an initial step towards a full regional reanalysis capability.
• UERRA (2014-2018) will provide a multidecadal, multivariate dataset of essential climate variables (ECVs) for the satellite era (1978-present).
• UERRA will include an ensemble regional reanalysis
• UERRA described as a component of a ‘pre-operational’ climate service, preparing the way for reanalysis as a central pillar of the Copernicus Operational Climate Service.
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Assessing uncertainties by evaluation against independent observational
datasets
DWD, KNMI, MI, EDI, UEA, NMA-RO, MO
EURO4M and UERRA GA 25- 27 March Exeter 2014
Andrea Kaiser-Weiss, DWD
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WP3 Objectives
• To evaluate deterministic, ensemble reanalyses and downscaled reanalyses through comparison to ECV datasets, that were derived independently
• To establish a consistent knowledge base on the uncertainty of reanalyses across all of Europe, by adopting a common evaluation procedure for ECVs, derived climate indicators, extremes and scales of variability that are of particular interest to users
• To statistically assess the provided information over Europe by applying the common evaluation procedure to the reanalyses products, gridded datasets and satellite data
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WP3 Objectives (cont.)
• To apply the common evaluation procedure for special climate features of selected sub-regions of Europe, providing feedback on the reliability of measures of uncertainty contained in reanalyses
• To synthesize the results of the evaluation into a general assessment of the reliability and uncertainty of regional reanalysis that guides users in the state-of-the-art application of the datasets produced in WP2
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Summary
1. Reanalysis useful climate services but we need to know the errors.
2. Need high quality datasets for validation, plus knowledge of their errors
3. Ensemble reanalysis should allow better characterisation of uncertainties
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Interest from UM partners
• India• South Korea• New Zealand
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© Crown copyright Met Office
Thank you for listening…
http://www.euro4m.eu/http://www.uerra.eu/