MLS and CALIOP Cloud Ice Measurements in the Upper Troposphere: A Constraint from Microwave on Cloud Microphysics

Wu, Dong L.; Lambert, A.; Read, W. G.; Eriksson, Patrick; Gong, Jie

doi:10.1175/jamc-d-13-041.1

Cited by 16 publications

(15 citation statements)

References 24 publications

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“…At all altitudes CALIOP seems to underestimate the IWC with respect to CloudSat. Wu et al (2014) show that the V3 CALIOP IWC agrees well with airborne in situ measurements up to approx. 20 mg m −3 at an altitude of 12 km.…”

Section: Output Data: Cirrus Properties From Caliopsupporting

confidence: 61%

Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

et al. 2017

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Abstract. Cirrus clouds play an important role in climate as they tend to warm the Earth-atmosphere system. Nevertheless their physical properties remain one of the largest sources of uncertainty in atmospheric research. To better understand the physical processes of cirrus clouds and their climate impact, enhanced satellite observations are necessary. In this paper we present a new algorithm, CiPS (Cirrus Properties from SEVIRI), that detects cirrus clouds and retrieves the corresponding cloud top height, ice optical thickness and ice water path using the SEVIRI imager aboard the geostationary Meteosat Second Generation satellites. CiPS utilises a set of artificial neural networks trained with SE-VIRI thermal observations, CALIOP backscatter products, the ECMWF surface temperature and auxiliary data.CiPS detects 71 and 95 % of all cirrus clouds with an optical thickness of 0.1 and 1.0, respectively, that are retrieved by CALIOP. Among the cirrus-free pixels, CiPS classifies 96 % correctly. With respect to CALIOP, the cloud top height retrieved by CiPS has a mean absolute percentage error of 10 % or less for cirrus clouds with a top height greater than 8 km. For the ice optical thickness, CiPS has a mean absolute percentage error of 50 % or less for cirrus clouds with an optical thickness between 0.35 and 1.8 and of 100 % or less for cirrus clouds with an optical thickness down to 0.07 with respect to the optical thickness retrieved by CALIOP. The ice water path retrieved by CiPS shows a similar performance, with mean absolute percentage errors of 100 % or less for cirrus clouds with an ice water path down to 1.7 g m −2 . Since the training reference data from CALIOP only include ice water path and optical thickness for comparably thin clouds, CiPS also retrieves an opacity flag, which tells us whether a retrieved cirrus is likely to be too thick for CiPS to accurately derive the ice water path and optical thickness.By retrieving CALIOP-like cirrus properties with the large spatial coverage and high temporal resolution of SEVIRI during both day and night, CiPS is a powerful tool for analysing the temporal evolution of cirrus clouds including their optical and physical properties. To demonstrate this, the life cycle of a thin cirrus cloud is analysed.

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Section: Output Data: Cirrus Properties From Caliopsupporting

confidence: 61%

Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

et al. 2017

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“…The PSD of McFarquhar and Heymsfield (1997) was selected both for this study and by Millán et al (2013). Recent results in Wu et al (2014) indicate that this PSD is less realistic than what is assumed in the CloudSat retrievals, at least for altitudes around 15 km.…”

Section: Discussionmentioning

confidence: 99%

Overview and sample applications of SMILES and Odin-SMR retrievals of upper tropospheric humidity and cloud ice mass

Eriksson

Rydberg²,

Sagawa

et al. 2014

Atmos. Chem. Phys.

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Abstract. Retrievals of cloud ice mass and humidity from the Superconducting Submillimeter-Wave Limb-Emission Sounder (SMILES) and the Odin-SMR (Sub-Millimetre Radiometer) limb sounder are presented and example applications of the data are given. SMILES data give an unprecedented view of the diurnal variation of cloud ice mass. Mean regional diurnal cycles are reported and compared to some global climate models. Some improvements in the models regarding diurnal timing and relative amplitude were noted, but the models' mean ice mass around 250 hPa is still low compared to the observations. The influence of the ENSO (El Niño-Southern Oscillation) state on the upper troposphere is demonstrated using 12 years of Odin-SMR data.The same retrieval scheme is applied for both sensors, and gives low systematic differences between the two data sets. A special feature of this Bayesian retrieval scheme, of Monte Carlo integration type, is that values are produced for all measurements but for some atmospheric states retrieved values only reflect a priori assumptions. However, this "allweather" capability allows a direct statistical comparison to model data, in contrast to many other satellite data sets. Another strength of the retrievals is the detailed treatment of "beam filling" that otherwise would cause large systematic biases for these passive cloud ice mass retrievals.The main retrieval inputs are spectra around 635/525 GHz from tangent altitudes below 8/9 km for SMILES/Odin-SMR, respectively. For both sensors, the data cover the upper troposphere between 30 • S and 30 • N. Humidity is reported as both relative humidity and volume mixing ratio. The vertical coverage of SMILES is restricted to a single layer, while Odin-SMR gives some profiling capability between 300 and 150 hPa. Ice mass is given as the partial ice water path above 260 hPa, but for Odin-SMR ice water content, estimates are also provided. Besides a smaller contrast between most dry and wet cases, the agreement with Aura MLS (Microwave Limb Sounder) humidity data is good. In terms of tropical mean humidity, all three data sets agree within 3.5 %RHi. Mean ice mass is about a factor of 2 lower compared to CloudSat. This deviation is caused by the fact that different particle size distributions are assumed, combined with saturation and a priori influences in the SMILES and Odin-SMR data.

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“…Avery et al . [] looked at CALIOP, MLS, and CloudSat cloud ice retrievals and concluded that MLS and CloudSat underreported small ice crystals relative to CALIOP, and MLS ice water content retrievals are quite noisy at high altitudes [also see Wu et al ., ]. HIRDLS and CALIOP data are the most relevant observational data sets for this study since they have better vertical coverage and are sensitive to small particles.…”

Section: Model Description and Data Usedmentioning

confidence: 99%

Cloud formation, convection, and stratospheric dehydration

Schoeberl

Dessler

Wang

et al. 2014

Earth and Space Science

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Using the Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis winds, temperatures, and anvil cloud ice, we use our domain-filling, forward trajectory model combined with a new cloud module to show that convective transport of saturated air and ice to altitudes below the tropopause has a significant impact on stratospheric water vapor and upper tropospheric clouds. We find that including cloud microphysical processes (rather than assuming that parcel water vapor never exceeds saturation) increases the lower stratospheric average H 2 O by 10-20%. Our model-computed cloud fraction shows reasonably good agreement with tropical upper troposphere (TUT) cloud frequency observed by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument in boreal winter with poorer agreement in summer. Our results suggest that over 40% of TUT cirrus is due to convection, and it is the saturated air from convection rather than injected cloud ice that primarily contributes to this increase. Convection can add up to 13% more water to the stratosphere. With just convective hydration (convection adds vapor up to saturation), the global lower stratospheric modeled water vapor is close to Microwave Limb Sounder observations. Adding convectively injected ice increases the modeled water vapor to~8% over observations. Improving the representation of MERRA tropopause temperatures fields reduces stratospheric water vapor by~4%. Trajectory models have demonstrated success at simulating many aspects of stratospheric H 2 O [e.g., Fueglistaler et al., 2005; Schoeberl and Dessler, 2011, hereafter SD11; Schoeberl et al., 2012, hereafter S12; Schoeberl et al., 2013, hereafter S13;Ueyama et al., 2014]. In the SD11 forward domain-filling trajectory model, winds determine the parcel motion and temperature determines the H 2 O content through instant adjustment of the parcel water vapor to not exceed predefined saturation limit. The assumption in this formulation is that any ice that forms quickly falls to lower atmospheric layers. We refer to this adjustment as instantaneous dehydration (ID).Air parcels moving slowly upward across the tropopause will have their water vapor concentration fixed as they pass through the cold point. However, convective systems can bypass the cold point and deposit ice directly into the lower stratosphere. Hydration through convection was parameterized in SD11 through a system described by Dessler et al. [2007]. In that system, parcels coincident with convection were set to the local saturation mixing ratio. Methane oxidation can also add water to the air parcel, but this process is unimportant in the lower tropical stratosphere where methane oxidation rates are slow (SD11).In SD11 and S12, we compared the modeled stratospheric water vapor to Microwave Limb Sounder (MLS) observations. These papers identified a number of processes that controlled stratospheric water vapor. These processes include the following: (1) the level of supersaturation permitted before ID, (2) the level of c...

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MLS and CALIOP Cloud Ice Measurements in the Upper Troposphere: A Constraint from Microwave on Cloud Microphysics

Abstract: . Because the microphysics assumption is critical in satellite cloud ice retrievals, the agreement found in the IWC-b 532 relationships increase fidelity of the assumptions used by the lidar and microwave techniques for upper-tropospheric clouds.

Cited by 16 publications

References 24 publications

Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

Overview and sample applications of SMILES and Odin-SMR retrievals of upper tropospheric humidity and cloud ice mass

Cloud formation, convection, and stratospheric dehydration

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