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Dale Barker, Richard Renshaw, Peter Jermey WWOSC, Montreal, Canada, 20 August 2014 Regional Reanalysis At The Met Office © Crown Copyright 2012 Source:

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1 Dale Barker, Richard Renshaw, Peter Jermey WWOSC, Montreal, Canada, 20 August 2014 Regional Reanalysis At The Met Office © Crown Copyright 2012 Source: Met Office© Crown Copyright 2014. Source: Met Office

2 Outline 1.Motivation – Why Regional Reanalysis? 2.EURO4M Regional Reanalysis Project (2010-2014) 3.Evaluation of pilot 2008-2009 European reanalysis 4.Regional Reanalysis Plans

3 © Crown Copyright 2014. Source: Met Office Motivation – Why Regional Reanalysis? 1.‘Complete’ datasets: Multivariate, gridded 4D climate datasets for users (DA combines model and observations in consistent manner). 2.Regional focus: Benefits of high- resolution (resolved orography, high-impact weather), local focus. 3.Model evaluation: Testbed for extended period (multi-decadal) evaluation of regional Weather/Climate Model. 4.New Science: Framework for developing/testing new scientific capabilities (e.g. precipitation accumulation assimilation). EURO4M: Model/DA: 12/24km ERA-Interim: Model/DA 80/125km

4 © Crown Copyright 2014. Source: Met Office EURO4M Regional Reanalysis EURO4M Participants: KNMI, UEA, URV, NMA-RO, MetO, SMHI, MF, MS, DWD. EURO4M leads: WP1 (Obs - UEA), WP2 (DA - MetO), WP3 (Products - DWD), WP4 (Project Management - KNMI). Strong partnership with ECMWF. Project duration April 2010 – March 2014. Perform proof-of-concept two year initial reanalysis (2008-2009 period). EURO4M/MetO Domain http://www.euro4m.eu

5 © Crown Copyright 2014. Source: Met Office Resolution: 12km UM vs 80km ECMWF model. Observations: Standard ERA/MO global obs, included radiances assimilated, plus: Additional high-resolution surface, sat. data: Precipitation (raingauge accumulations, radar) Cloud fraction, Visibility Statistical post-processing of surface fields: Introduces local effects of orography. Inclusion of additional surface mesonet obs. Correct model bias through e.g. Kalman Filter. Raw data for tailored European climate information bulletins (WP3 users). EURO4M WP2 (DA-reanalysis): What can we add to ERA?

6 © Crown Copyright 2014. Source: Met Office Model: 12km UM Data Assimilation: 24km 4DVAR Surface fields: SST and sea-ice from OSTIA UM soil moisture and snow Lateral boundary conditions: 6-hourly ERA-Interim global analyses Observations: Surface (SYNOP, buoy, etc), Upper air (sonde, pilot, wind profiler), Aircraft, AMV (‘satwinds’), GPS-RO and ground- based GPS, Scatt. winds, Radiances (ATOVS, AIRS, IASI, MSG clear sky) UM Regional Reanalysis: Scientific Configuration 4DVAR 4DVAR resolution: 24 vs 36 km

7 © Crown Copyright 2014. Source: Met Office UM Regional Reanalysis: Technical Configuration UM ported to ECMWF HPC. Observations from ERA BUFR, LBCs from ERA GRIB. Archive output fields to MARS. ECMWF ODB used for observation monitoring. 4 six-month streams (2008-2009 pilot reanalysis) MetO’s ECMWF HPC allocation.

8 © Crown Copyright 2014. Source: Met Office Monthly Means MO ERATTRangeWindPrecipRH2PMSL Evaluation of 2008-2009 pilot reanalysis: Climate Statistics

9 © Crown Copyright 2014. Source: Met Office © Crown copyright Met Office Climate Statistics Climate stats – ETCCDI/WMO & ECA&D/KNMI Monthly Means TRangeT Wind Precip RH2 PMSL

10 © Crown Copyright 2014. Source: Met Office Monthly Means MO ERAT Compare with ECA&D statistics from obs stations Evaluation of 2008-2009 pilot reanalysis: Climate Statistics

11 © Crown Copyright 2014. Source: Met Office Compare with ECA&D statistics from obs stations Mean Temp Mean of Min Temp Mean Max Temp Mean Wind Speed Max of Min Temp Max of Max Temp Min of Min Temp Max Precip 5Days Summer Days Calm Days DaysPrecip>10m m DaysPrecip>20m m Max of Min Temp Mean Precip Icing Days Total Wet Precip Mean Temp Range Tropical Nights Mean Wet Precip Mean Cloud Mean Rel Hum Maximum Gust Max Daily Precip Wind Days Dry Days Wet Days Frost Days Mean PMSL 24/24 months 23/24 months 22/24 or 21/24 mns 20/24 or 19/24 mns 10/24 months MO better in… Evaluation of 2008-2009 pilot reanalysis: Climate Statistics

12 © Crown Copyright 2014. Source: Met Office APGD, GPCC, CRU and E-Obs are Observation-Based Climatologies Francesco Isotta

13 © Crown Copyright 2014. Source: Met Office Above: APGD, E-Obs Observation-Based Climatologies Below: Regional Reanalysis Climatologies (no precip assimilation) Francesco Isotta

14 © Crown Copyright 2014. Source: Met Office EURO4M UM reanalysis UM downscaler (ECMWF ICs) EURO4M HIRLAM reanalysis ERA-INTERIM UM Climate Run (No analysis) Impact of Resolution and DA on Precipitation Analysis Skill Monthly skill (ETS) during 2008 Resolution Assimilation 1mm4mm8mm16mm 1%9%50%300 % 8%13%14%30%

15 © Crown Copyright 2014. Source: Met Office ERA-InterimHIRLAMMESANMESCANMO Truth is gridded 24hr rain gauge observations (ECMWF) Rel Diff(Model,ERA-Interim) = (Model – ERA-Interim)/ERA-Interim Fractional Skill Score

16 © Crown Copyright 2014. Source: Met Office 6hr At low thresholds models over-represent At high thresholds models under-represent, but … … bias is reduced by increased resolution & 4DVAR assimilation Frequency Bias

17 © Crown Copyright 2014. Source: Met Office Control – MO (3DVAR) Test – MO (4DVAR) FSS averaged over three months 3DVAR vs 4DVAR Comparison

18 © Crown Copyright 2014. Source: Met Office 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 both deterministic and the first ensemble regional reanalysis (leveraging techniques developed for global NWP). UERRA, like ERA-CLIM2 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.

19 © Crown Copyright 2014. Source: Met Office Goal: Improved understanding and modelling of the Indian monsoon. Funding from Indian Ministry of Earth Sciences. Collaboration with NCMRWF (modelling/DA) and IMD (data recovery). Developed in parallel with, and contribute to, NCMRWF’s regional UM NWP application. Leverage UM regional reanalyses efforts performed in the ‘sister’ UERRA European regional reanalysis. IMDAA includes a >35yr deterministic reanalysis (1978-present). Indian Monsoon Data Assimilation and Analysis (IMDAA) Project: 2014-2018 IMDAA June 2013 Uttarakhand Rainfall Skill

20 © Crown Copyright 2014. Source: Met Office Summary 1.Motivations for regional reanalysis similar to those for regional NWP. 2.Very promising early results from EURO4M pilot 2008-2009 study. 3.Impact of 4DVAR regional reanalysis largest for HIW e.g. intense rainfall. 4.Current efforts in Met Office regional reanalysis a.UERRA: ‘Production’ European regional reanalyses (1978-present). b.Indian Monsoon UM regional reanalysis project to begin in 2014. c.RR for other areas under consideration.

21 © Crown Copyright 2014. Source: Met Office Thanks. Any Questions?

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