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

Outline

1. Why Regional Reanalysis ?

2. EURO4M Regional Reanalysis – Evaluation

3. Regional Reanalysis Plans

• Uncertainty Estimation in Regional ReAnalysis (UERRA) project

What is a reanalysis ?

state-of-the-art NWP

past observations

gridded analyses

• 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 ?

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• ...

ERA Interim ReanalysisDee et al, 2011

• Global atmosphere, T255 (80km), 60 vertical levels• 12-hour 4D-Var• 1979 - present

© Crown copyright Met Office

1. Why Regional Reanalysis ?

Evidence from operational NWP

25km Global

vs

12km NAE

...the benefits of resolution

forecast range

screen temperature rms error (K)

global 25km

NAE 12km

...and the disadvantage of boundaries!

forecast range

mean sea level pressure

rms error (Pa)

global

NAE

• 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

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

Increase in resolution

EURO4M: Model/DA: 12/24kmERA-Interim: Model/DA 80/125km

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

Getting more from surface obs...

• Visibility• Cloud• Rainfall

© Crown copyright Met Office

2. EURO4M Regional ReanalysisEvaluation

Peter Jermey

Russian heatwave, July 2010

Tmax, 10-07-2010

e-obs

www.ecad.eu

KNMI, Ge Verver et aI

ECAD: European Climate Assessment and Dataset

Daily data from 1950 -

Maximum temperature on 10 July 2010 (during the Russian heat wave)

obs daily 25km grid

“E-Obs”

Tmax10-07-10

ERA-Interim

12km EURO4M

obs

© Crown copyright Met Office

Climate Statistics

Monthly Means

MO

ERA T

Compare with ECA&D statistics from obs stations

© 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

© 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

© Crown copyright Met Office

Floods July 200823-26th July

Accumulations

SYNOP ERA-Interim UKMO

15mmMean abs error

13mm

© Crown copyright Met Office

ETS precip scores

6hr

ERA-Interim

HIRLAM

Met Office

Truth is SYNOP rain gauge data

© 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

Concept, methods and results

Deutscher Wetterdienst (DWD)

Jörg Trentmann, Jennifer Lenhardt

Evaluation of EURO4M Reanalysis data using Satellite Data

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°)

Mean differences, cloud cover,MetOffice - CM SAF (AVHRR), July 2008/9

29EURO4M Final Assembly – 03/2014

Mean differences, precipitation,MetOffice - CM SAF, July 2008

30EURO4M Final Assembly – 03/2014

Mean differences, water vapour,MetOffice - CM SAF (ATOVS), July 2008/9

31EURO4M Final Assembly – 03/2014

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

© Crown copyright Met Office

Regional Ensemble

Uncertainties from ensembles

Calibrate for variables we can validate

Get uncertainties for variables we can’t

validate

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.

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.de

Andrea Kaiser-Weiss, DWD

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

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

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

Interest from UM partners

• India• South Korea• New Zealand

© Crown copyright Met Office

Thank you for listening…

http://www.euro4m.eu/http://www.uerra.eu/