1. Diagnosing latent heating rates from model and in-situ microphysics data: Some (very) early results Chris Dearden University of Manchester DIAMET Project Meeting 15/11/11

2. Intro
So far looked at B647 and B648; Ran mesoscale model simulations (3km horizontal resolution); Diagnosed contributions to latent heating/cooling from the parameterized microphysical processes; For B647  derived some latent heating rates from the in-situ (2D-S) data; What is shown here is a summary of these preliminary results

3. B647

4. B647: Convective cloud band case over Irish Sea
Output temperature tendencies from each microphysical process directly, e.g.
Diagnose average latent heating rates along 54N for selected microphysical processes
Accumulated surface precip between 11am – 11:30am

5. B647 Average latent heating rates (Ks-1) at 54N, between 11 – 11:30am
(derived from mesoscale model simulation)
Condensation
Evaporation

6. B647 Average latent heating rates (Ks-1) at 54N, between 11 – 11:30am
(derived from mesoscale model simulation)
Deposition
Sublimation

7. B647 Average latent heating rates (Ks-1) at 54N, between 11 – 11:30am
(derived from mesoscale model simulation)
Freezing
Melting

8. B647: Deriving latent heating rates from in-situ data
Assume hydrometeor size distributions from 2D-S can be described by Marshall-Palmer distribution:
–> fit intercept and slope parameters, i.e. obtain values of and
Then use them to calculate microphysics process rates (and thus latent heating rates) based on model parameterizations

9. B647: Calculating heating rates from in-situ data
B647 Flight path
Red line indicates region of interest where 2D-S records significant ice and liquid water - calculate intercept and slope parameters for this section
T and altitude along red flight transit

10. Ice number concentration from 2D-S vs integrated Marshall-Palmer fits
Note that assuming negative exponential size distribution tends to overestimate the total ice no. concentration by ~ a factor of 2. Maybe better to use gamma function in future.

11. Condensation
Still need to know supersaturation (qv – qsat_liq). Need to know vertical velocity for this...
Condensation timescale (s), depends on N_0 and Lambda

12. Deposition
Tau_dep depends on N_0 and Lambda for ice; also depends on ice fallspeed parameters (to account for increased vapour flux due to ventilation effects)
Assume ice is big enough to fall as 'snow', i.e , where a_s=11.72 and b_s=0.41;
Assume at water saturation, so q_v = qsat_liq;
Can then derive the latent heat associated with deposition growth:

13. Riming
Tricky one, since the riming efficiency depends on the fallspeeds of liquid drops as well as the falling ice crystals
Really need CDP data to get cloud droplet size distribution (haven't looked at this yet); for now just use 2D-S 'round' category and assume these liquid drops are large enough to fall as 'rain', such that
where a_r = ; b_r = 0.8;
This enables calculation of LH rate due to collisions between falling drops and snow;
NB - snow collecting cloud droplets is neglected here for now

14. Heating rates from in-situ data
Riming
Mean = K/s
St.dev = K/s
Deposition
Mean = K/s
St.dev = K/s
So riming comes out about an order of magnitude more than deposition at this altitude

15. How does this compare with the model?
Deposition
Freezing
Heating rate around -8degC ~ K/s (cf ~0.002K/s from in-situ data)
Max heating rate ~0.02K/s (cf ~0.013K/s from in-situ data, but in the wrong place)

16. B648

17. B648: Banded precipitation case
Accumulated surface precip between 4pm – 4:30pm

18. B648 Model results: Average latent heating rates at 50N (4pm – 4:30pm)
Deposition
Freezing
Condensation
Evaporation
Sublimation
Melting

19. Tentative conclusions
Latent heating rates associated with specific microphysical processes have been derived from both model and in-situ data (with some necessary assumptions and caveats)
Where comparisons are possible, the model produces reasonable heating rates associated with depositional growth of ice;
Comparison of riming rates, although not yet complete, hints that the model may be getting vertical distribution of heating wrong?
Need vertical velocities to derive condensation heating rates for in- situ data; modelling results suggest this is a dominant contributor to the total diabatic heating