met office progress report - ralatlantic tropical cyclone track density (transits/month) for n96,...

© Crown copyright Met Office

Met Office progress report

Andy Brown

WGNE, Boulder, October 2011


Outline

•  Production systems

•  Upgrades

•  GA cycle and seamlessness

•  Research issues

•  Impact of resolution (seasonal/climate)

•  Ocean-Atmosphere coupling (impact on NWP; lessons for longer range)

•  Aerosols – what complexity is justified?


Met Office production systems

© Crown copyright Met Office © Crown copyright Met Office

Operational NWP Models: Jun 2011

Global  25km 70L  4DVAR – 60km  60h forecast twice/day  144h forecast twice/day  +24member EPS at 60km 2x/day

NAE  12km 70L  4DVAR – 24km  60h forecast   4 times per day   +24member EPS at 18km 2x/day

UK-V (& UK-4)  1.5km 70L  3DVAR (3 hourly)  36h forecast   4 times per day


Parallel Suite 25 : Nov 2010 Global Data Assimilation - 4DVAR to 60km; CovStats from EC Ensemble Seasonal Forecast Model to L85 (from L38)

Parallel Suite 26 : Mar 2011 Global Model – GA3.1 – Removal of Spurious Light Rain Problem; improved hydrological cycle UK models - Improvements to Drizzle/Fog (eg drop number linked to aerosol) Seasonal Forecast System - more members for 30 day forecast Post-processing - Best Data via Blending/Lagging - 5000 sites UK4 run as Global Model Downscaler

Parallel Suite 27 : Jul 2011 Global DA– Hybrid Data Assimilation + Moisture Control Variable Global Model – Non-interactive Prognostic Dust UK DA – Doppler Radar Winds UK Model – further microphysics

Model Upgrade Highlights


PS26 : Light rain package Total precip. rate 20/10/2010 East Pacific

Before

After


PS27: Impact of Package Components Combined Winter/Summer Results

Hybrid – coupling with MOGREPS for estimating model error

Moisture control variable, replacing RH with scaled humidity variable

Introduce METARS

GOES/Msat-7 clear-sky radiances, extra IASI (land)

Revisions to MSG clear-sky processing and GPSRO

Reduced spatial thinning (ATOVS/SSMIS/IASI/AIRS/aircraft)

NWP index vs obs +1.4

0

+0.8

+0.1

+0.2

+0.2


PS26 - Drizzle

Before After


PS26 Global Downscaling T+96 valid 12z Friday 17 June 2011

Benefits •  Seamless Product •  Topographic detail

•  Orographic Precipitation •  Inland Penetration of Showers (snow) •  Better scores than UK4 for many parameters (not visibility)


Global  16-20km 85L (85km top)  Hybrid 4DVAR (50km inner-loop)  60 hour forecast twice/day  144 hour forecast twice/day  36/12member 40km MOGREPS-G 4*/day

MOGREPS-EU  Common NWP/reanalysis domain.  12Km 70L (40km top)  3D-Var (or NoDA)  48 hour forecast   12 members ; 4 times per day

UKV  1.5km 70L (40km top)  3DVAR (hourly)  36 hour forecast   4 times per day  12 member 2.2km MOGREPS-UK

Operational NWP Configs: Spring 2013 (Tentative)


How seamless to be?


GlobalAtmos 3.0 CORDEX intercomparison

Uses GA3.0 in regional configurations of climate

model

GlobalAtmos Configuration Evolution/use of the GlobalAtmos “trunk”

GlobalAtmos 3.1 Minor diffs in NWP

configuration GlobalAtmos 4.0

GlobalAtmos 4.1 NWP diffs persist

GlobalAtmos 5.0

CMIP intercomparison Atmospheric component

uses GA4.0

Academic collaborator Starts 3-year PhD project

using GA4.0

Main versions (3.0, 4.0 etc) designed for use for all global applications (NWP, seasonal, decadal…..)

Only branch when essential – and then development route remains main trunk


Differences between GA3.0 and GA3.1

•  GL3.0 uses 9 land surface tiles whereas GL3.1 uses 1.

•  GA3.1 uses longer tails for stable boundary layers over land.

•  GA3.1 does not include the enhanced treatment of CO2 and O3 LW absorption.

•  Different bare soil and sea ice roughness lengths.

•  GL3.1 does not have representation of sub-grid heterogeneity in its run-off.

i.e. differences small – physics virtually identical for weather and climate


Systematic investigation of impacts of resolution on seasonal and climate predictions

Malcolm Roberts, Adam Scaife, Keith Willaims (MO); Pier-Luigi Vidale (NCAS)

Atlantic tropical cyclone track density (transits/month) for N96, N216 and N320 models (31 years), and obs/reanalyses


Composite of DJF El Niño and La Niña events (>1.2σ)

HadISST N216L85-O025L75 (60km – 1/4°)

N96L85-O01L75 (130km - 1°)

westward extension is much improved in high resolution


North Atlantic SST bias in coupled model


Zonal mean wind bias


High resolution seasonal predictions

90W 0 90E

Higher resolution model :

-  Better representation of Gulf Stream

-  More atmospheric realistic mean state

-  Better representation of blocking

0.30

0.15

0.00

Winter blocking frequency

Observations

150km model

60km model


Model resolution summary •  Atmosphere

•  Changes in atmospheric mean state between 130 km60km40km resolutions are on the whole relatively small

•  Variability (and extremes) vary more as resolution is increased

•  Ocean •  Fundamental change from 1° to 1/4° resolution as

Rossby radius becomes better resolved, allowing dissipation to be significantly reduced

•  Coupled •  Improvements to mean state can have significant

impact on variability (e.g. blocking) – Scaife et al, 2010, 2011 (in revision)

•  Implications for climate change projections


Coupled NWP research


Coupled NWP research: Motivation

•  Potential additional predictability/skill from representing air-sea physical coupling (MJO, diurnal cycle, TCs…)

•  Quantify cost-benefit for NWP skill

•  Tackle and improve coupled process errors at "source“ – leading to improvement of systematic errors and drifts in model predictions for longer timescales

•  “Transpose-CMIP”

•  Seamless prediction: more unified experimental design and model science from NWP through seasonal to climate prediction timescales

•  Framework for developing and applying new coupled data assimilation techniques


Coupled model DJF bias at day 30

GloSea4 seasonal DJF bias

Next step: use of short-range coupled model errors to inform climate model development

Coupled model DJF bias at day 4


Aerosols: what complexity is justfied for what application?

Simple Land/Sea

climatologies

CLASSIC aerosol

climatologies

Replacement of climatologies with prognostic schemes based on CLASSIC •  Dust (Operational 2011) •  Sea-salt (Next in line) •  Biomass burning (Future)

Little resemblance

to reality

Reasonable monthly means

but no relation to meteorology

MACC/GEMS Assimilated aerosol for

initial conditions

DA of fires for biomass burning

Prognostic UKCA-MODE upgrades for other aerosol

species

2001-2008

2008-2011

Current Fully prognostic driven by

meteorology

Long-term upgrades


Questions?


Details of PS26 global changes (physics to GA3.1)


PS26 Global Model changes

•  Light rain package: •  Revised convection diagnosis •  Prognostic rain •  Abel/Shipway fall speeds •  1 microphysics iteration every 2 minutes •  PC2 bug fixes

•  Version 3Z radiation, with Lean SW and McICA •  Frictional heating term •  JULES (science neutral)


PS26 : Light rain package Total precip. rate 20/10/2010 N. Atlantic

Before

After


PS26 : Light rain package Total precip. rate 20/10/2010 East Pacific

Before

After


PS26 Global Model Light Rain - Summary

•  Improved ppn ETS for light thresholds

•  Improved mean ppn (reduced) and dry day occurrence (increased)

•  Cloud amount and Precip benefits, especially in Climate Runs

•  ETS Cloud Base scores worse


Details of PS27 global assimilation changes


PS27: Global Data Assimilation Upgrade

•  Assimilation method • Hybrid – coupling with MOGREPS for estimating model

error • Moisture control variable, replacing RH with scaled

humidity variable •  Observation changes

•  Introduce METARS • GOES/Msat-7 clear-sky radiances, extra IASI (land) • Revisions to MSG clear-sky processing and GPSRO • Reduced spatial thinning (ATOVS/SSMIS/IASI/AIRS/

aircraft)


PS27 Hybrid data-assimilation

•  Basic idea: Use data from MOGREPS-G to improve the representation of background error covariances in global 4D-Var:

MOGREPS COV

•  MOGREPS is sensitive to the position of the front, and gives covariances that stretch the increment along the temperature contours.

•  Ensemble currently too small to provide the full covariance, so we blend the MOGREPS covariances with the current climatological covariances; i.e., we use a hybrid system:

Climatological COV

u response to single u observation:

Hybrid COV


PS27 Hybrid data-assimilation

•  Tuning options:

•  Relative weight of climatological and ensemble covariances.

•  80% climatological / 50% ensemble covariance in the troposphere (designed to maintain the analysis fit to observations). Relax to the full climatological covariance above 21km.

•  The spatial localisation of the ensemble covariances (to reduce affect of sampling noise.). We use the following localisation functions:

Horizontal Vertical (Vertical variance)


Dec uncoupled: +1.2

Pre-operational hybrid trials Verification vs. obs

Better/neutral/worse

NH TR SH Dec uncoupled (29 days) 29/94/0 6/117/0 12/109/2 Jun coupled (28 days) 34/89/0 9/114/0 46/74/3

Jun coupled: +1.6

Skill:

RMSE:


Dec uncoupled: -4.0

Pre-operational hybrid trials Verification vs. own analyses

Better/neutral/worse

NH TR SH Dec uncoupled (29 days) 16/91/16 7/69/47 3/106/14 Jun coupled (28 days) 49/63/11 9/86/28 18/82/23

Jun coupled: -0.2

Skill:

RMSE:


Pre-operational hybrid trials Verification vs. ECMWF analyses

Own analyses

Dec uncoupled trial

ECMWF analyses

Very poor tropical scores against our own analyses are not reflecting the “real” performance.

Skill:

RMSE:

-4.0 +1.7


PS27 Moisture control variable Limits on q/RH skew distribution

•  Plot shows O vs B B vs A similar

•  Near 0% or 100% RH, (A-B) is very skewed

•  Transform to a function of (A+B)/2 (Holm) – distribution is much more symmetric

•  This makes the analysis nonlinear

•  Much better fit of humidity-sensitive satellite obs to background

•  Reduced spin-down of precipitation


PS27: Impact of Package Components Combined Winter/Summer Results

Hybrid – coupling with MOGREPS for estimating model error

Moisture control variable, replacing RH with scaled humidity variable

Introduce METARS

GOES/Msat-7 clear-sky radiances, extra IASI (land)

Revisions to MSG clear-sky processing and GPSRO

Reduced spatial thinning (ATOVS/SSMIS/IASI/AIRS/aircraft)

NWP index vs obs

NWP index vs anl

+1.4 -1.9

0 +1.2

+0.8 +0.4

+0.1 +0.1

+0.2 -0.2

+0.2 +0.4


Details of PS26 and PS27 UK model upgrades


PS26 and PS27: UK Model Upgrade DrFog+

•  Radiation upgrade - Neutral •  SkyView:- better representation around sunrise/sunset in mountain areas. •  Upgrade to 3Z scheme. •  Lean Wenyi spectral files

•  Microphysics upgrade •  Abel & Shipway fall speed: More realistic (slower) fall speed of drizzle,

allows more time to evaporate and reduces slight drizzle rates. •  Coupled Murk-Autoconversion: Cloud drop number related to aerosol

concentration. Removes land-sea split in drizzle and favours fog formation in polluted air.

•  Enable droplet taper to reduce number of droplets in lower boundary layer

•  Data Assimilation – Small Impact •  New COVSTATS, New Humidity Control Variable •  Doppler Radial Winds, METARs, High Res ASCAT


PS26 - Drizzle

Before After


PS27 DrFog - Sea Fog

Before After


PS27 DrFog - Land Fog, Winter

Before After

Obs


PS 26 DrFog Visibility Verification


200m threshold

1000m threshold


DrFog Verification Summary

•  Index impact: -0.45% (Combined for Summer, Winter and Autumn trials)

•  Visibility: -2.92% (200 m. better, 1000 m. worse, 4000 m. worse). •  Cloud amount: +1.17% (0.3 worse, 0.5 better, 0.8 better). •  Cloud base: +3.36% (100 m., 500 m. and 1000 m. better) •  Wind: -0.14% •  Temperature: -0.21%

•  Pros: •  Removes spurious drizzle •  Much better representation of winter fog and sea fog •  Removes spurious very low visibilities (<50 m.) in all seasons.

•  Cons: •  Worsens under-forecasting of summer/autumn radiation fog with

adverse impact to overall verification scores


PS26 Post Processing

Previous Best Data Rules for combining models •  Use the most recent highest resolution model at any given lead time

•  Use nowcasts to T+6 •  Then UKPP form UK4 from T+7 to T+36

•  Then NAE from T+36 •  Then Global from T+60

• Then MOGREPS-15 from T+144 •  For probabilities (e.g. PoP)

•  MOGREPS-R to T+36 •  Then MOGREPS-G from T+36

•  Update hourly using latest available Nowcast and Models


PS26: Post Processing Introduce Blending

•  Between longest forecast horizon (T+360) and T+0 we have •  30 MOGREPS-15 runs •  14 Global runs •  10 NAE runs •  4 MOGREPS-R runs •  6 UK4 runs •  6 Nowcasts

•  For any given forecast horizon, want to make good use of available models

•  Current BestData = a * latest forecast + (1-a)* previous BestData

•  ‘a’ varies with lead time and model combination


Surface Temperature Jumpiness (Mean abs difference from 6hrs ago) – blending from Apr 2011

nowcasts

2 cycles/day mixing with 4 cycles/day

met office progress report - ralatlantic tropical cyclone track density (transits/month) for n96,...

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