© Crown copyright Met Office Assimilating cloud affected infrared radiances at the Met Office Ed Pavelin and Roger Saunders, Met Office, Exeter.

Crown copyright Met Office Assimilating cloud affected infrared radiances at the Met Office Ed Pavelin and Roger Saunders, Met Office, Exeter Satellite data used in NWP (1) April 2014 Observation typeSatellitesNWP models * AMSU/MHS radiances 3 NOAA (15/18/19) + 2 MetopG, R HIRS clear radiances2 MetopG, R IASI and AIRS clear+cloudy radiancesMetop + AquaG, R ATMS & CrIS radiancesSuomi NPPG SSMIS radiancesF16 was used until failure, will substitute F17/F18 G, R Geo imager clear IR radiancesMSG, MFG, GOES, MTSAT2G, R, UK GPS RO bending angles5 COSMIC, Metop/GRAS, GRACE-A, TerraSAR-X, CNOFS G, R GPS ZTDs~350 European stationsG, R, UK * G=Global, R=regional=Europe, UK=UK area Satellite data used in NWP (2) April 2014 Observation typeSatellitesNWP models * AMVs geo5 geo satellitesG, R, UK AMVs MODIS and AVHRRAqua, Terra, NOAA, MetopG, R Scatterometers: sea-surface windsMetop/ASCAT, Oceansat-2/OSCAT was used G, R, UK MW imager sea-surface winds: WindsatCoriolisG, R SEVIRI cloud height/amountMSG R, UK SSTs: AVHRR, AMSR-ENOAA, Metop, AquaG, R, UK Soil moisture: ASCATMetopG, R, UK Sea ice: SSM/I, SSMISDMSPG, R Snow covervariousG, R * G=Global, R=regional=Europe, UK=UK area Crown copyright Met Office Background IR sounders provide the biggest impact for GOBAL NWP short range forecasts (next slide) An important objective is to use more of the data over CLOUD and land areas Assimilation of cloud information from IR sounders is currently limited by: Primitive representation of cloud in radiative transfer calculations Unrealistic representation of cloud structure in NWP models Lack of knowledge of background error covariances for cloud variables Weaknesses of variational assimilation methods for highly non-linear observation operators Crown copyright Met Office This plot shows the impact per day of various observation types on global 24hr fcast There is a large impact from IASI on MetOp-A, CrIS on Suomi-NPP and AIRS on Aqua Forecast impacts of satellite observations Crown copyright Met Office Cloudy IR radiances: Radiative effect Clear (Cloudy Clear) 14 m14.8 m9.6 m7.2 m4.5 m4.1 m Cloudy LW T Sounding BT ~ >10K 14 m14.8 m9.6 m7.2 m4.5 m4.1 m Cloud signal~ >10K Temperature O-B~ 0.1K Need: Accurate forward model Knowledge of background error covariances for cloud Very challenging to assimilate cloudy radiances without degrading T/q analysis! Crown copyright Met Office Cloudy IR radiance assimilation 1. Clear scenes only 2. Clear channels only 3. Grey cloud methods Cloud Jacobian Height Cloud Jacobian Height B. Variational all-sky schemes C. Ensemble DA methodsA. Improved forward models Height Crown copyright Met Office 1D-Var Grey Cloud Analysis (Met Office scheme) Aim: To extract T and q information in the presence of cloud Simple cloud model: Single-level grey cloud Overcast component Clear-sky component Analyse two cloud variables: n eff, p ctp Retrieve n eff, p ctp in 1D-Var to fit observed radiances (No direct assimilation of cloud information) Crown copyright Met Office Problems with grey cloud model using all channels (results from 1D-Var simulation) Severely degraded analysis below cloud Errors in cloud analysis aliasing into T and q FG AN FG AN FG AN FG Crown copyright Met Office Limitations in cloud model In many cases, 1D-Var cloud model is unrealistic Not (generally) single-level grey cloud Cloud is generally multi-level, 3D Leads to large biases below cloud top Solution: Remove channels most likely to be poorly modelled Simple automatic channel selection: Reject all channels peaking below retrieved cloud top 10% of weighting function area allowed below cloud top Channel selection carried out for each sounding Effect of inhomogeneous cloud (from Prates et al., 2014) Crown copyright Met Office Met Office cloudy IR scheme CF CTP 4D-Var CTP, CF Retrieve cloud parameters in 1D-Var Using RTTOV: Single-level grey cloud Choose channels with