1. Incrementing moisture fields with satellite observations
Stefano Migliorini
Met Office

2. 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

3. 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

4. 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

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

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

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

8. 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

9. Treatment of moisture in variational data assimilation

10. 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.).

11. 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

12. 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

13. 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

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

15. 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

16. 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:

17. Linear diagnostics of moisture
Global ensemble of 44 T+6 perts on 6 March UTC

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

19. Linear diagnostics of moisture
Consistency tests (same ensemble)

20. Linear diagnostics of moisture
q’cl increments

21. Linear diagnostics of moisture
q’cf increments

22. Linear diagnostics of moisture
q’ increments

23. 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

24. Thanks for your attention