Evaluation of parcel model estimatedcloud top height of deep convective
clouds with active sensor observation
Seiji Kato1, David P. Duda2, Kuan-Man Xu1, Seung-Hee Ham2,Sunny Sun-Mack2, Yan Chen2, and Walt F. Miller3
1NASA Langley Research Center
2Analytical Mechanics Associates, Inc.

3ADNET Systems, Inc

Pre-Launch EarthCARE Science and Validation Workshop

Deep convective cloud cloud top height

Takahashi and Luo, 2012, GRL

Motivations• How well can a parcel model predict deep convective cloud height?
• What is the uncertainty in outgoing longwave irradiance when thecloud top height predicted by a parcel model is used?

Outline of this talk
• Deep convective cloud objects• Cloud top height of deep convective clouds derived from CALIPSO andCluodSat observations• Comparison of observed cloud top height with the level of neutral
buoyancy as a function of CAPE
• Uncertainty in outgoing TOA longwave irradianceData set used in this study• MERRA-2 (MERRA2_300.inst3_3d_asm_Nv.YYYYMMDD.nc4)• Version 4 CALIPSO and R05 CloudSat used in CCCM (Version D1)• July 2006 through June 2010 (4 years of data)• CALIPSO-CloudSat merged cloud profiles

Ham et al. 2021

Deep convective cloud objects
Cloud objects are derived from daytime MODIS Aqua observations

Xu et al. 2005 J. Climate

Geolocation of daytime small and large deep convective cloud objects

There is no significant regional difference where smaller and larger deep convective clouds occur.

Convective available potential energy (CAPE)• CAPE is computed with MERRA-2 temperature and water vapor profiles inside cloudobjects• Buoyancy integrated from the level of free convection (LFC) to the level of neutral
buoyancy (LNB)• CAPE is the maximum work done by buoyancy

𝐿𝑁𝐵
CAPE = −𝑅𝑑 ‫𝑣𝑇 𝐶𝐹𝐿׬‬,𝑝𝑎𝑟𝑐𝑒𝑙 − 𝑇𝑣,𝑒𝑛𝑣𝑑𝑙𝑛 𝑝

Deep convective clouds over ocean
Red: Equivalent diameter > 300 km (large DCC)
Bleu: 100 < equivalent diameter < 150 km (small DCC)
• Cloud top heightincreases with CAPE.
• Large DCC cloud top is
0.84 km higher thansmall DCC cloud top
when CAPS is larger
than 1000 J kg-1

Error bars indicates min and max values among cloud objects

Comparison with level of neutral buoyancy• Level of neutral buoyancy: equivalentpotential temperature is equal to theenvironmental temperature• Small DCC cloud top height is lower than
LNB by 0.02 km when CAPE is larger than
1000 J kg-1
• Large DCC cloud top height is higher than
LNB by 0.38 km when CAPE is larger than
1000 J kg-1

TOA albedo and outgoing longwave irradiance
AlbedoOutgoing longwave irradiance

Rate of decreasing OLR with height
Small deep convective clouds: 10.9 Wm-2 km-1
Large deep convective clouds: 11.6 Wm-2 km-1
If we use LNB is lower by 400m, using LNB overestimate
outgoing longwave irradiance by ~4 Wm-2.

Standard deviation of TOA longwave anomalies
A 4 Wm-2 bias is about 20%of the monthly mean TOA
upward longwave
irradiance variability fortropical western/central
Pacific

Summary and conclusion• Mean observed cloud top height is 0.38 km higher (0.02 km lower) thanthe level of neutral buoyancy for large (small) convective clouds with CAPE
larger than 1000 J kg-1.
• Mean cloud top height estimated by a parcel model is within 400 m from observedcloud top heights.

• Large DCC mean cloud top is higher than the level of neutral buoyancy andsmall DCC cloud top height is lower than the level of neutral buoyancy for agiven CAPE
• The rate of decreasing upward longwave irradiance with height is 10.9 Wm2 km-1 for small deep convective cloud objects and 11.6 Wm-2 km-1 for large
deep convective cloud objects.
• Because the upward longwave irradiance decreases approximately 11 Wm-2
km-1, the -0.4 km cloud top height bias is ~+4 Wm-2 in the upward longwaveirradiance bias.
• Do observations of vertical velocity, higher resolution of temperature andhumidity reduce the bias?

backups

Entropy production within deep convective clouds
When water vapor condenses under saturated conditions in thermal equilibrium, the process is isentropic.
Other processes changes entropy

Entropy source

Expression

Conditions

Entropy change(W kg-1 K-1)

Diffusion 𝑠ሶ𝑑𝑖𝑓 oftemperature by airhydrometeor (liquid)
conduction

𝑟𝑖 𝑐𝑙 (𝑇𝑙 −𝑇)2
𝑇𝑙 𝑇𝜏𝑐𝑙Liquid water content 5 g m-3
Air temperature 265 K
Hydrometeor temperature 275 K
Conductive time scale 100 s

5.7 × 10−42𝑉𝑇𝑙

Liquid water mixing ratio 0.3 ×10−3
Terminal velocity 10 ms-1
Rain rate 10 mm hr-1

1.1 × 10−4

Diffusion 𝑠ሶ𝑑𝑖𝑓 of kinetic
energy through airhydrometeor drag force(rain drops)𝑟𝑙𝜏𝑣𝑙 𝑇Diffusion 𝑠ሶ𝑑𝑖𝑓 oftemperature in moist air𝑘𝑇(𝛁𝑇 ∙ 𝛁𝑇)
𝜌𝑎 𝑇 2

Conductivity 32 × 10−4 𝑊𝑚−1 𝐾 −1 6.9 × 10−6
Temperature gradient 10 K m-1

Radiative heating𝑞ሶ 𝑟𝑎𝑑𝑇

Radiative heating 1 K day-1
Air temperature 273 K

4.2 × 10−5