Incrementing moisture fields with satellite observations Stefano Migliorini Met Office

Outline Focus on moisture-sensitive obs in all-sky conditions Moist assimilation control variable design A new physical-statistical model to diagnose moisture and cloud water assimilation increments First tests

Motivation and challenges Clouds are an ubiquitous atmospheric feature: a better use of cloud-affected satellite radiances should lead to significant NWP skill improvements (within the predictability limits implied by the scales of the analysed features) Very challenging goal: (T, cloud) and (q, cloud) nonlinearities, cloud overlapping uncertainties, large systematic errors IASI WV channel T jac cloudy clear RMS(OmB) IASI atm window channel (921) q jac latitude

Predicted SEVIRI 10.8 µm T+6 at 06 UTC NWP of cloud fields (1/4) Predicted SEVIRI 10.8 µm T+6 at 06 UTC

NWP of cloud fields (2/4) SEVIRI 10.8 µm at 06 UTC

NWP of cloud fields (3/4) Predicted SEVIRI 6.2 µm at 06 UTC

NWP of cloud fields (4/4) SEVIRI 6.2 µm at 06 UTC

Radiative effects of clouds Spectral response All-sky predictions of IR water vapour channels are ”safer” In general (MW and IR), need for robust relation between moisture increments in model and obs space clear cloudy difference

Treatment of moisture in variational data assimilation

Moisture in NWP Moisture in the atmosphere generates cloud (and precip), which interacts with solar and infrared radiation, also affecting surface temperatures, gives rise to heating and cooling perturbations: waves and turbulence affecting large scale temperature distribution. Mesoscale convection ensures vertical transport of moisture, heat and momentum. Model resolution too coarse to determine cloud from model fields: parametrization schemes (Rhcrit) Courtesy C. Morcrette By analysing in-situ data collected by research aircraft, considering different length flight legs, calculating the leg-averaged thermodynamic properties and comparing them to the individual thermodynamic measurements one can estimate RHcrit as a function of grid-box size. Extrapolation of the fit suggests that RHcrit reaches 1.00 when the grid-box is around 180 m (Ian Boutle, pers. comm.).

Use of moisture-sensitive obs For NWP, errors in radiative balance less important than errors in frequency, amount and timing To correct such errors, crucial to make use of moisture-sensitive observations To do so, a consistent methodology to assimilate these obs is needed Current assimilation schemes deal with uncorrelated ``control variables’’ (CV) that are Gaussian and unbiased Lack of well-defined balance relationships, bounded pdf, bias makes choice of moist CV challenging

RHT forecast error distribution Close to saturation, RH-like CV preserves cloud when no moisture obs present Distribution of RHT biased and skewed: suboptimal assimilation Hólm transform: p(rh | (rhb+rha)/2) Dependence on analysis makes transform nonlinear: iterative method needed Moist CV also needs to be uncorrelated to other CVs and linearly related to model variable incs Courtesy A Lorenc and B Ingleby

Definition of a moist CV Model variables: u, v, w, θ, П, ρ, q, qcl, qcf, Cl, Cf, Ct Perturb. forecast (PF) model vars: (u’, v’, w’, θ’, П’, ρ’, q’T) = w’ w’ components not independent: we need uncorrelated vp and transform Up such that w’ = Up vp Met Office CVs: vp = (ψ’, χ’, pA’, μ’) = stream function, velocity potential, unbalanced pressure, humidity variable h regression coefficient between and : uncorrelated; a ~ 1/σ : more Gaussian

Increments of moisture fields At model level 12 q’T rh’T /σ(rh’T) ~ μ‘

Interface with model and obs (1/2) PF model increments (q’T) need to be partitioned into increments of water phases to be used by NWP model and radiative transfer operator Current (nonlinear) incrementing operator based on linearization of the Smith (1990) cloud liquid water parametrization scheme unsatisfactory Problems in the UKV UM configuration (precipitation spin-down): switched off, all increments to vapour, with cloud liquid and frozen water fields unchanged Assimilation of moisture-sensitive obs should rely on an improved incrementing operator (no need to be nonlinear now) the so-called precipitation spin-down problem manifests in an excess of precipitation following the generation of moisture assimilation increments

Interface with model and obs (2/2) New design based on linear relation between q’cl and q’T Then (q’cl)diag = a q’cl where a is regression between global ensemble perturbations From similar expression for frozen cloud increments:

Linear diagnostics of moisture Global ensemble of 44 T+6 perts on 6 March 2015 1200 UTC

Linear diagnostics of moisture q’cl regression coefficients q’cf regression coefficients

Linear diagnostics of moisture Consistency tests (same ensemble)

Linear diagnostics of moisture q’cl increments

Linear diagnostics of moisture q’cf increments

Linear diagnostics of moisture q’ increments

Summary In VAR water phases analysed via single moist control variable: need for a diagnostic relation to partition the increments into different water phases Key requirement for two major projects using VAR: all-sky assimilation of satellite data Convective-scale 4DVar Essential for 4DEnVar, which will also be a tool within LFRic Present increment op complex nonlinear code that is not performing well: either limited or switched off (as in the UKV) First tests of new linear physical-statistical increment op

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