Retrieval of liquid water cloud properties from ground-based remote sensing observations

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Retrieval of liquid water cloud

properties

from ground-based remote sensing

observations

cloud pr

operties

Christine Knist

Invitation

You are kindly

invited to attend my

Ph.D. defense

entitled:

Retrieval of liquid

water cloud

properties from

ground-based

remote sensing

observations

On Monday

September 29, 2014

at 10:00 in the

Senaatszaal of the

Auditorium

at the Delft

University of

Technology

Before the defense

from 9:30 till 9:45,

I will give a short

talk about the

contents of the thesis.

A reception will take

place after the

defense.

Christine Knist

c.l.knist@tudelft.nl

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ground-based remote sensing observations

Proefschrift

ter verkrijging van de graad van doctor aan de Technische Universiteit Delft,

op gezag van de Rector Magniﬁcus prof. ir. K. C. A. M. Luyben, voorzitter van het College voor Promoties,

in het openbaar te verdedigen op maandag 29 september 2014 om 10:00 uur

door

Christine Luise KNIST

Diplom-Meteorologin, Christian-Albrechts-Universit¨at Kiel, Duitsland geboren te Hofgeismar, Duitsland.

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Samenstelling promotiecomissie:

Rector Magniﬁcus, Voorzitter

Prof. dr. ir. H. W. J. Russchenberg, Technische Universiteit Delft, promotor

Prof. dr. H. J. J. Jonker, Technische Universiteit Delft

Prof. dr. A. P. Siebesma, Technische Universiteit Delft; KNMI, De Bilt

Prof. dr. A. Macke, Leibniz Institute for Tropospheric Research, Leipzig

Prof. dr. S. Crewell, University of Cologne

Dr. D. P. Donovan, KNMI, De Bilt

Dr. R. Boers, KNMI, De Bilt

This research was supported by the Klimaat voor Ruimte Program

ISBN 978-94-6259-263-6

Retrieval of liquid water cloud properties from ground-based remote sensing observations. Dissertation at Delft University of Technology.

All rights reserved. No parts of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the author.

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Abstract

Accurate ground-based remotely sensed microphysical and optical properties of liquid wa-ter clouds are essential references to validate satellite-observed cloud properties and to im-prove cloud parameterizations in weather and climate models. This requires the evaluation of algorithms for retrieval of cloud microphysical and optical properties using ground-based remote sensing observations, because there are large differences between the cloud property retrievals of various algorithms due to the differences in the applied retrieval theories, as-sumptions, retrieval inputs and constraints. This thesis focuses on three commonly used ver-tical cloud models for the parameterization of the in-cloud verver-tical structure in cloud property retrieval schemes. The objective is to explore the impact of the vertical cloud models on the computations of microphysical and optical properties of liquid water clouds and to evaluate their uncertainties. This information can help to improve current liquid water cloud property retrieval schemes and to increase the accuracy of the obtained cloud physical properties.

A comparison of three algorithms with different vertical cloud models for the retrieval of liquid water cloud microphysical and optical properties is performed. In the first algo-rithm, the vertical structure of the cloud is parameterized as being vertically homogeneous (Vertically Uniform, VU). In the second algorithm, the used vertical cloud model originates from an adiabatic model (Scaled Adiabatic Stratified, SAS) and the third algorithm relies on a vertical model, which considers the impact of cloud top entrainment mixing processes on the cloud microphysical properties (Homogenous-Mixing, HM). All three algorithms use observations of the cloud radar reflectivity, the microwave radiometer obtained liquid water

path (LWP) and the cloud geometrical thickness from lidar and cloud radar. They require a

priori assumptions on the cloud droplet size distribution (DSD). Hence, the gamma function is used to parametrize the DSDs and possible values for the gamma DSD shape parameter are evaluated from reanalyzed in-situ observations. All three algorithms investigated here

retrieve vertical proﬁles of the liquid water content (LWC ), the droplet concentration, the

effective radius, the visible optical extinction and the visible optical depth.

The differences between the cloud property retrievals of each algorithm are explained on the basis of remote sensing observations that appear to be typical for low-level water clouds. The results of the VU cloud model per se lack detailed information on the vertical distribution of the cloud property retrievals. Under adiabatic conditions, the retrievals of the SAS and the

HM models are equivalent, while the vertical distributions of theLWC , the effective radius

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the cloud boundaries. The droplet concentrations of the SAS and HM models are very close to each other for both conditions. The model of uniform cloud properties yields values of the droplet concentration that are 25% lower than those from the models of non-uniform cloud properties. Interestingly, the differences between the cloud microphysical properties lead to very similar values of the retrieved visible optical depths. Sensitivity and error analyses suggest that the droplet concentration retrieval is generally most strongly affected by errors

in the radar reﬂectivity and theLWP, while the retrievals of the effective radius are most

robust in all three models. The retrievals of the optical depth and the effective radius are less affected by the variations in the DSD shape parameter as compared to the impact of the errors in observations. In contrast, the droplet concentration is more sensitive to changes in the gamma DSD shape parameter. Consequently the DSD shape parameter should be known a priori with reasonable precision.

In order to evaluate the validity of the cloud property retrievals, the three algorithms are applied to synthetic surface remote sensing observations of a modeled liquid water cloud layer. The retrievals are compared with the physical properties of the modeled cloud layer as a function of the cloud height. Applying the algorithms to the best estimate “observations” and the assumed value for the DSD shape parameter leads to consistent HM model cloud

property retrievals. In turn, signiﬁcant overestimations of the SAS modelLWC (50%) and

the effective radius (10%) occur at cloud top where the SAS model retrieves the maximum values in the proﬁles. In all layers below the cloud top, the SAS cloud model retrievals of theLWC and the effective radius are very close to the modeled ones, because the true

prop-erties are increasing nearly adiabatically. As expected, the differences in theLWC and the

effective radius profiles are largest upon the application of the VU model, which significantly overestimates both properties in the lower levels and underestimates them in the upper height levels. The very simple assumption that all cloud properties are uniformly distributed leads to a significant underestimation of the droplet concentration by about 20%. The SAS model droplet concentration is only slightly overestimated by 7%. Nevertheless, all cloud model retrievals of the optical depth agree well with those of the modeled cloud layer.

To evaluate the performance of the cloud property retrievals obtained from real remote sensing observations, a broadband shortwave (SW) radiation closure analysis is performed for a selected water cloud case study. The SW ﬂuxes at the surface calculated on the basis of the cloud properties of VU, SAS and HM models agree well with the surface radiation observations. The mean difference between the simulated and the measured SW ﬂuxes is

2 W/m2_{to 5 W/m}2_{with a standard deviation of 13 W/m}2_{. The uncertainty in the simulated}

ﬂuxes can be explained by the uncertainty in theLWC and the effective radius due to errors

in theLWP, the reﬂectivity and the assumption on the gamma DSD shape parameter. The

three presented retrieval methods provide reliable cloud optical depth values for the selected

water cloud case study. The different vertical distributions of the LWC and the effective

radius, as well as differences in the droplet concentration, have a minor effect on simulating SW ﬂuxes, because they lead to similar values of the optical depth.

The present work shows that the liquid water cloud property retrievals obtained from the remote sensing observations depend on the model that is used to describe the vertical cloud

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structure. It shows that systematic deviations between the microphysical cloud properties of the VU, SAS and HM cloud models exist, especially regarding the droplet concentration, the

LWC , the effective radius and the optical extinction at the cloud boundaries. The cloud

mi-crophysical properties estimated using the HM model parametrization show the best perfor-mance. The SAS cloud model can represent the vertically resolved microphysical properties well if they are very close to being adiabatic. Clearly, there are significant deviations in the cloud microphysics from the clouds that are parameterized as being vertically homogeneous (VU model). The different combinations of the microphysical properties in the three mod-els lead to almost equivalent VU, SAS and HM optical depth retrievals, which agree well with the values of the modeled liquid water cloud. They are all able to reproduce the surface shortwave broadband radiative flux. However, by modeling clouds as being vertically ho-mogeneous, sufficient accuracy in both the microphysical and the optical property retrievals cannot be achieved.

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Samenvatting

Nauwkeurige waarnemingen door remote sensing technieken vanaf de grond van de micro-fysische en optische eigenschappen van waterwolken zijn essentieel voor de validatie van satellietwaarnemingen en voor de parameterisatie van wolken in klimaat-en weermodellen. Dit vereist een evaluatie van de in gebruik zijnde algoritmes, want er zijn hierbij grote ver-schillen in toegepaste theorie en de aannames. Dit proefschrift concentreert zich op drie modellen die vaak worden gebruikt voor de parameterisatie van de verticale structuur van de wolken.

De impact van die parameterisaties op de nauwkeurigheid van de uiteindelijke retrievals wordt in dit proefschrift besproken. Er wordt een vergelijking gemaakt tussen drie algo-ritmes met verschillende wolkenmodellen: Vertically Uniform (VU), waarin de wolken niet variren als functie van de hoogte; Scaled Adiabatic Stratiﬁed (SAS), waarin de wolk adia-batisch verandert; Homogeneous Mixing (HM), waarin ook de vermenging van de wolk met de omgeving wordt beschouwd.

Alle algoritmes gebruiken als input de radar reﬂectivity, het vloeibare-waterpad dat volgt uit een microgolf-radiometer en de wolkendikte zoals gemeten door de combinatie van radar en lidar. Er wordt hierbij een gamma-druppelgrootteverdeling (DSD) aangenomen. De pa-rameters van deze verdeling komen uit een her-analyse van in situ waarnemingen. De al-goritmes geven verticale proﬁelen van de vloeibaar-waterinhoud, de druppelconcentratie, de effectieve straal, de optische uitdoving en de optische dikte van de wolk.

De verschillen tussen de retrievals zijn onderzocht aan de hand remote sensing waarne-mingen van typische laaghangende waterwolken. De resultaten van het VU model geven per deﬁnitie geen hoogte-informatie. Onder adiabatische omstandigheden zijn de resultaten van de SAS en de HM algoritmes equivalent. Onder niet-adiabatische omstandigheden kunnen er grote verschillen optreden, in het bijzonder aan de wolkengrenzen. De druppelconcentraties die worden bepaald met het SAS en HM model zijn vergelijkbaar. Die van het VU model zijn 25% kleiner. De optische dikte is voor alle drie de modellen ongeveer gelijk. Uit een gevoeligheidsanalyse volgt dat fouten in de radar reﬂectie en het vloeibaar-waterpad vooral doorwerken in de druppelconcentratie. De effectieve straal is er minder gevoelig voor. Een goede aanname van de vorm-parameter van de druppelgrootteverdeling is vooral van belang voor de bepaling van de concentratie.

De drie algoritmes zijn getest aan de hand van gesimuleerde waarnemingen van een syn-thetisch wolkendek met bekende eigenschappen. Vervolgens zijn de gesimuleerde

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waarne-mingen gebruikt als input voor de retrieval algoritmes. De uitkomsten zijn vergeleken met de werkelijke parameters van de wolken. Het HM model geeft consistente resultaten. Het SAS model leidt tot een signiﬁcante overschatting van de LWC (50%) en de effectieve straal (10%) aan de wolkentop. Lager in wolk neemt de afwijking af, omdat de wolk zich daar zich meer adiabatisch gedraagt. De verschillen tussen HM en SAS enerzijds en het VU model anderzi-jds is groot. De aanname dat de wolk verticaal uniform is, leidt tot een onderschatting van de druppelconcentratie met 20% (tegenover 7% voor SAS). Alle modellen geven nauwkeurige schattingen van de optische dikte.

De performance van de algoritmes is ook gevalueerd aan de hand van echte wolken door de broad-band short wave ﬂux te meten en te vergelijken met de uitkomsten van de algoritmes. Voor alle modellen komt de gemeten waarde overeen met de berekende. Het gemiddelde verschil is 2–5 Watt per vierkant meter met een standaarddeviatie van 13 Watt per vierkante meter. De onzekerheid is een gevolg van de fout in LWC en de effectieve straal. De verticale structuur heeft maar een klein effect op de gesimuleerde SW-ﬂuxes.

Dit proefschrift laat zien dat de bepaling van de micro-fysische eigenschappen van wa-terwolken afhangt van het model van de verticale structuur. Het HM model geeft de beste resultaten, in het bijzonder aan de randen van de wolkenlaag. Het SAS model werkt goed voor adiabatische bewolking. Bij het VU model kunnen er grote afwijkingen optreden. Dit laatste is niet het geval voor de optische eigenschappen: alle modellen leiden hier tot gelijk-waardige retrievals.

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Chapter 1 Introduction

1.1 Motivation

Adequate representation of cloud physical properties in climate models is an essential factor for accurately simulating the radiative transfer in the cloudy atmosphere in order to deter-mine the impact of clouds on the Earth’s radiative energy balance and to predict future cli-mate change. This is reﬂected in the recently published Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC), in which it is stated that “Clouds and aerosols continue to contribute the largest uncertainty to estimates and interpretations of the Earth’s changing energy budget” (IPCC, 2013, in Chap. 7 p. 573). This is related to the fact that “Many cloud processes [their representations] are unrealistic in current Global Climate Models, and as such their cloud response to climate change remains uncertain” (IPCC, 2013, in Chap. 7 p. 584). The large uncertainty contributed by clouds requires im-provements of clouds in terms of their macrophysical properties (e.g., fractional coverage, cloud boundaries) and their microphysical properties, like the particle size distribution, the concentration of the cloud particles and their sizes and the amount of liquid water. Both the cloud macrophysical and the microphysical properties characterize the cloud optical proper-ties, such as the optical depth, that are used to determine the effects of clouds on the incoming shortwave radiation of the Sun and on the longwave radiation emitted by the earth-atmosphere system.

Liquid-phase boundary layer clouds, such as stratus and stratocumulus, are known to sig-nificantly contribute to the uncertainty in climate sensitivity estimates among current climate models (Nam et al., 2012; Brient and Bony, 2012, 2013). Low level water clouds are widely distributed over the Earth’s surface, especially in the polar regions and over the Eastern parts of the subtropical oceans, where they occur in extensive and persistent sheets (Warren et al., 2007; Eastman and Warren, 2010; Eastman et al., 2011). The prevalence of these cloud types leads to them playing a significant role in the Earth’s energy balance (Hartmann et al., 1992) while their radiative consequences are particularly difficult to simulate in climate models. On the one hand, they reflect incoming shortwave radiation back to space, which has a cooling effect on the Earth’s surface (cloud albedo effect). On the other hand, they absorb part of the

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longwave radiation emitted by the earth-atmosphere and reradiating it back to the surface, which has warming effect on the Earth’s surface. The resulting radiative effect depends on many factors, particularly on the macrophysical, microphysical and optical cloud properties. The extensive marine subtropical cloud fields that often consist of small liquid water droplets with high droplet number concentrations have significant optical depth values and thus a strong albedo effect (Chen et al., 2000; Hahn et al., 2001). Several climate sensitivity studies revealed that climate models often underestimate the cloud cover and overestimate the optical depth values, which is known as the “too few, too bright” tropical low-cloud problem in climate models (Nam et al., 2012; Brient and Bony, 2013). The latter can be caused, among other factors, by inaccurate cloud microphysical properties, such as the droplet sizes and the liquid water path, which have a strong impact on the optical depth (Nam et al., 2012). Thus their simulation and impact on the Earth’s radiation budget under changing environmental conditions results in a broad spread of responses (Nam et al., 2012; Brient and Bony, 2012, 2013). Compared to the shortwave albedo impact of the subtropical boundary layer clouds, the low-level Arctic clouds have a warming effect on the Earth’s surface and can induce the loss of sea and surface ice, because they emit part of the longwave radiation back to the Earth at temperatures that are warmer than the underlying surface (Shupe and Intrieri, 2004). The recently published study of Bennartz et al. (2013) has shown that the low level water clouds enhanced the extended loss of the Greenland ice sheet observed in July 2012. The nature of their optical depth values and the liquid water path on the one hand enhanced the surface longwave emission and on the other hand allowed the transmittance of sufficient shortwave radiation to the surface. Both effects lead to an increase of the surface temperature above the freezing level (Bennartz et al., 2013). The role of Arctic cloud processes and their impact on the surface radiation budget contributes to large differences among current model climate models (Karlsson and Svensson, 2013).

The existence of aerosol cloud feedback mechanisms is an additional complication to capture low level water clouds correctly in models. An increase in aerosol number density may lead to an increase of the droplet concentration and to a decrease of the particle sizes when the amount of liquid water content is constant. This modifies the optical properties so that the cloud albedo increases and more incoming radiation of the sun is reflected back to space (Twomey, 1977; Twomey et al., 1984). Besides the alteration of the cloud radiative ef-fect, the modified cloud microphysics suppress precipitation formation, which in turn causes an increase of the cloud life time (Albrecht, 1989).

As the result of the diversity in the macrophysical, microphysical and optical properties and the difficulty in representing them in climate models, low-level boundary layer clouds and their impact on the Earth’s radiation balance present a large uncertainty in future climate projections. Although significant advances have been made in climate models associated with the parametrization of low cloud microphysical processes and their interaction with aerosols after the previous IPCC Assessment report (AR4, IPCC, 2007), there is still low confidence in the representation and in the quantification of these processes in the climate models (IPCC, 2013).

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To support the improvement of the representation of cloud microphysical processes and the understanding of the cloud microphysical responses to aerosol changes, long-term and continuous observations of clouds are needed in order to characterize the cloud physical properties on local and global scales. Active remote sensing instruments, like cloud radars, lidars together with passive sensors, like radiometers, are well suited for cloud observations and have the advantage that they are continuously operated over long periods from the space and the ground. Ground and spaceborne sensors provide observations of the atmospheric state at several wavelengths of the electromagnetic spectrum and at different temporal and spatial resolution. The availability of these observations made it possible to develop techniques that can retrieve the microphysical and the optical properties at different resolution for various types of clouds. A large variety of retrieval techniques have emerged over the last 25 years, which rely on different retrieval theories, assumptions, parameterizations and use either single or multi sensor observations as input parameter.

Several studies in the past have shown that satellite radiometer observations in the visible and near infrared electromagnetic spectrum provide information on the microphysical and optical properties of boundary layer liquid-phase clouds, such as the cloud optical depth and the particle size of the droplets (e.g. Nakajima and King, 1990), the cloud liquid water path, the geometrical thickness and the droplet concentration (e.g. Boers et al., 2006; Bennartz, 2007; Roebeling, 2008). This has opened the option to provide large scale cloud climatolo-gies (e.g. Rossow and Schiffer, 1999; Roebeling, 2008) and to examine long-term trends in the cloud properties, since the space-based remote sensing observations from meteorological satellites cover large areas or the entire globe. However, climate studies require cloud prop-erties of sufficient accuracy, since even small differences in the cloud parameterizations can substantially affect climate model estimates of the cloud feedback (IPCC, 2007). The men-tioned passive space-based techniques rely on assumptions about the vertical distribution of the cloud properties, because the satellite radiometer observations provide only limited infor-mation on the in-cloud vertical structure. Depending which vertical cloud model is assumed, differences in the retrieved particle size of the droplets and in the cloud liquid water path can occur (Nakajima and King, 1990; Bennartz, 2007). The vertical distributions of the cloud microphysics vary considerably in time and space, because the properties are influenced by a complex interplay of many factors, like the cloud formation mechanisms, the environmental background conditions, the dynamics and the entrainment mixing processes (e.g. Pawlowska and Brenguier, 2000; Pawlowska et al., 2000, 2006). Consequently, it is rather difficult to characterize the vertical distribution of the cloud properties over large scales.

Active remote sensors, such as cloud radars and lidars, provide vertically resolved cloud observations that in addition to the observations from the passive instruments provide valu-able information on the vertical cloud structure. One of the recent active satellite instru-ment is the cloud radar CloudSat, which was launched in April 2006 (Stephens et al., 2002). Retrieval algorithms have been developed which provide proﬁles of liquid water cloud mi-crophysical properties, such as the liquid water content, from the CloudSat radar obser-vations alone (Austin and Stephens, 2001; Austin et al., 2009). Nevertheless, the detec-tion capability of low-level liquid water clouds by the current space-borne cloud radar is

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very limited, because the radar reﬂectivity of these clouds is often below the minimum

detectable signal of− 28 dBZ or the clouds are masked by surface contamination

(Chris-tensen et al., 2013). The lack of observations leads to biases in retrieved low cloud prop-erties from CloudSat alone (Christensen et al., 2013). The A-train constellation of various satellites (Stephens et al., 2002) is currently used to combine active and passive satellite sensor observations in order to evaluate the cloud properties derived from the single sen-sors and to improve the retrievals for climate monitoring and for cloud parameterizations in climate and weather models (Christensen et al., 2013; Roebeling et al., 2013). Further-more, an array of passive and active instruments with improved capabilities will be aboard of the Earth Cloud Aerosol Radiation Explorer (EarthCARE) satellite mission (ESA, 2004), which is planned to be launched in 2017.

Simultaneous and continuous measurements from co-located active and passive sensors are acquired at ground-based remote sensing sites that are operated at different locations across the globe by several research programs, like the U.S. Department of Energy’s Atmo-spheric Radiation Measurement (ARM) program (Ackerman and Stokes, 2003), the Cloudnet program (Illingworth et al., 2007) and the ACTRIS network (Gaetani, 2012). The multi-sensor observations at high temporal and vertical resolution are used in a number of schemes for retrieving vertical proﬁles of the cloud properties for different types of clouds (e.g. Frisch et al., 1998; Liao and Sassen, 1994; Mace and Sassen, 2000; L¨ohnert et al., 2004; Illingworth et al., 2007; Dunn et al., 2011).

The ground-based remotely sensed vertically resolved cloud properties at high tempo-ral and vertical resolution on local scales enable to evaluate the single sensor space-based observations (e.g. Dong et al., 2008; Roebeling, 2008) and can further help to assist the im-provement of cloud parameterizations in weather forecast models and in climate models (e.g. Morcrette et al., 2012). The outstanding problem related to this opportunity is that recently published inter-comparison studies of existing ground-based remote sensing retrieval algo-rithms have found large discrepancies between the obtained cloud properties, particularly for the cloud liquid water and the particle sizes and in their vertical distributions (Turner et al., 2007a; Huang et al., 2012; Zhao et al., 2012). The major reason for the large differences lies in the differing algorithm theory bases and assumptions used to infer the vertically resolved cloud properties from the remote sensing observations (Huang et al., 2012; Zhao et al., 2012). This complicates the use of the various surface remotely-sensed cloud products as unique ref-erences for further validation purposes. Thus, more research attention is currently focused on a better understanding of the cloud retrieval differences and on the development of a common approach for evaluating the retrieval uncertainties and limitations (e.g. Turner et al., 2007a; Zhao et al., 2011, 2012; Huang et al., 2012; Mather et al., 2012). This will help to further improve and optimize the cloud property retrieval products.

For the reasons mentioned before, the work presented in this thesis is focused on liquid water cloud microphysical and optical properties and the ability to retrieve them from co-incident active and passive surface remote sensing observations under different assumptions of the in-cloud vertical structure. Therefore a common data basis of surface remote sensing observations is integrated into three vertical cloud models, which return different vertical

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proﬁles of the cloud microphysical properties. The vertical cloud models considered in this work are widely used in satellite and ground-based retrievals schemes. The purpose is to review the different cloud models and to demonstrate the impact of the assumed in-cloud vertical structure on the microphysical retrieval computations. The uncertainties and limi-tations in the cloud property retrievals based on the cloud models are assessed in terms of synthetic observations of a modeled cloud layer and on the basis of a shortwave radiation closure analysis.

1.2 Liquid water cloud microphysical and optical characteristics

Low-level stratiform and stratocumuls clouds are mainly formed in the planetary boundary layer underneath low-level temperature inversions. They typically have vertical extents of a few hundred meters and the temperatures of the marine clouds are often comparable to those of the underlying surface. The global annual fractional coverage of stratocumulus clouds is over the oceans 22% and over the continents 12% (Warren et al., 2007; Eastman and Warren, 2010; Eastman et al., 2011). The fractional coverage, the altitude (cloud boundaries) and the cloud temperature are typically considered as the cloud macrophysical properties.

As a result of the global frequency of these cloud types, their significant impact on the Earth’s radiation balance and the difficulties in characterizing them in models, their micro-physical properties have been studied extensively in several aircraft measurement campaigns over the last 20 years at different locations around the globe. For instance, in the First In-ternational Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE; Austin et al., 1995), in the first and second Aerosol Characterization Experiment (ACE-1, ACE-2; Brenguier et al., 2000b), in the Baltex Bridge Cloud Campaigns (BBC-1, BBC-2; Crewell et al., 2004) and in the recently conducted campaign called RACORO (Vogelmann et al., 2012), which provided the first extended-term aircraft dataset of a wide range of continental boundary layer clouds, and aerosol properties for climate studies.

The reminder of this section brieﬂy introduces the liquid water cloud microphysical prop-erties and gives an overview of the characteristics that appear to be typical in the in-situ measurements of warm boundary layer clouds. The cloud optical properties, like the optical depth, that are of main importance for studying cloud-radiative effects, are also considered. The standard deﬁnitions presented here are described in more detail in Chap. 2 and 4.

Liquid-phase clouds mainly consist of small spherical liquid water droplets that are in-homogeneously distributed in the vertical and horizontal directions. The basic parameter used to interpret the microphysical state of liquid water clouds is the droplet size distribution (DSD). The droplet size distribution is expressed as n(r), such that n(r) dr is the radius r and

r + dr. In many cases the droplet size distributions of liquid water clouds containing droplet

sizes of few micrometers are mono-modal with long tails at large particle sizes (Miles et al., 2000). It is very common in cloud physics to characterize the droplet size distribution in terms of its moments as they can be related to the remote sensing observations under certain assumptions (Chap. 2). To express the properties of the droplet size distribution in this sense,

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one commonly uses the ath radius moment of the droplet size distribution: ra₌ _∞ 0 ra_{n(r) dr} _∞ 0 n(r) dr = N−1 _∞ 0 ra_{n(r) dr,} _(1.1)

where N is the total number concentration:

N =

_∞

0

n(r) dr. (1.2)

The range of observed values of N in liquid water clouds is very broad (10 /cm3_{– 1000 /cm}3_).

The cloud droplet concentration strongly depends on the locally observed cloud condensation nuclei (CCN) and their chemical compositions. Various aircraft in-situ observations at differ-ent locations demonstrated that the CCN concdiffer-entration over the oceans are generally smaller than in more polluted continental background conditions. Accordingly, the cloud droplet

con-centrations observed in marine clouds are smaller (≈ 50 /cm3_{– 150 /cm}3_{) compared to their}

continental counterparts (e.g. Chuang et al., 2000; Miles et al., 2000; Brenguier et al., 2000b; Liu and Daum, 2002).

Using Eq. (1.1), the mean, surface mean and volume mean radius of the DSD are:

r_m=r1, r_s=r221, and r_v =r313. (1.3)

The area-weighted mean radius of the cloud droplets is the effective radius r_e. It is deﬁned

as the ratio of the third to the second moments of the DSD (Hansen and Travis, 1974):

r_e= _∞ 0 r3_{n(r) dr} _∞ 0 r2_{n(r) dr} =r 3 r2 = r3 v r2 s . (1.4)

Liquid water clouds are often divided into different groups with common droplet radii classes. Water cloud droplets produced by CCN activation and condensational growth have generally radii smaller than 20 μm and the DSD is expected to have a mono-modal form (Pruppacher and Klett, 1997). Water clouds composed of such small droplet sizes are designated as non-drizzling liquid water clouds. The presence of larger droplet sizes increases the frequency

of collisions between cloud particles and hence the formation of drizzle (r_e≥ 25 μm), heavy

drizzle (r_e≤ 250 μm) or rain (r_e> 250 μm) drops. The collisional droplet growth generates

a second mode in the DSD at the larger particle sizes i.e., the drizzle mode (Pruppacher and Klett, 1997). Water clouds containing particle sizes larger than 25 μm are designated as drizzling water clouds (French et al., 2000). The in-situ observations and detailed model simulations showed that an effective radius of around 15 μm presents a threshold value above

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drizzle drops often occur in liquid water clouds (Pinsky and Khain, 2002; Masunaga et al., 2002; Pawlowska and Brenguier, 2003). Due to the different environmental conditions and formation mechanisms between marine and continental clouds, it appeared to be typical that maritime water clouds contain larger effective radii coherent with the smaller number concen-trations compared to the continental ones (Martin et al., 1995; Snider and Brenguier, 2000; Liu and Daum, 2002). Representative mean droplet radii for maritime clouds are 12 μm and for continental clouds are 6 μm (e.g. Miles et al., 2000). Hence marine stratus clouds have a higher potential for producing drizzle or rainfall (Fox and Illingworth, 1997).

The integration over the mass of the DSD results in the liquid water content (LWC ):

LWC = 4 3πρw _∞ 0 r3_{n(r) dr =} 4 3πρwNr 3_, _(1.5)

where ρw is the density of liquid water. The liquid water content varies typically between

0.01 g/m3 _{and 1 g/m}3_{, which strongly depends on the vertical position in the cloud (e.g.}

Pawlowska and Brenguier, 2000; Pawlowska et al., 2000). As mentioned earlier, the vertical

distributions of the cloud microphysics, such as the effective radius and theLWC , depend

on a complex interplay of many factors (e.g. Pawlowska et al., 2006). Including the determi-nation of the cloud properties for vertically inhomogeneous clouds, the deﬁnitions above can be readily extended to functions of height h above cloud base height. An integration of the

LWC over the cloud height results in the liquid water path (LWP): LWP =

_h_ct

hcb

LWC (h) dh, (1.6)

where h_cbis the height at cloud base and h_ctis the height at cloud top. Various cloud

cli-matology analyses revealed that theLWP of water clouds often ranges between 50 g/m2_and

250 g/m2(e.g. Kollias et al., 2007; Ebell et al., 2011).

The basic optical cloud properties, which characterize radiative effects of liquid water clouds, are the extinction coefﬁcient and the cloud optical depth, respectively. The attenua-tion of a sufﬁciently narrow beam of solar radiaattenua-tion traversing a cloud layer is expressed by the Beer-Bouguer-Lambert law (Kokhanovsky, 2004):

I_λ= I0,λexp (−τλ/cos(θ0)) (1.7)

where I_0,λis the intensity of the incident solar radiation, I_λis the intensity of radiation after

passing the cloud layer, θ0is the solar zenith angle and τλis the cloud optical depth, all given

for a wavelength λ. For a vertically inhomogeneous cloud layer, the optical depth is deﬁned as:

τ_λ=

_h_ct

hcb

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where σext,λ is the extinction coefﬁcient. The extinction coefﬁcient for a given DSD and

vertical position in the cloud is (Hansen and Travis, 1974):

σ_ext,λ=

_∞

0

Q_ext,λ(r)πr2n(r) dr, (1.9)

where Qext,λ is the extinction efﬁciency. The extinction efﬁciency is the ratio of the

ex-tinction cross section and the geometrical cross section (πr2). It is obtained from Mie theory,

which is valid for spherical particles having sizes that are comparable or larger than the wave-length of the incident radiation (van de Hulst, 1957; Bohren and Huffman, 2008). At visible wavelengths of the electromagnetic spectrum (0.4 μm – 0.7 μm), it is known that the extinc-tion efﬁciency asymptotes to approximately 2 as the particle sizes become much larger than the wavelength of the incident radiation (geometrical optics limit) (e.g. Hansen and Travis, 1974). Thus for typical water cloud droplet sizes between 5 μm and 15 μm, the extinction

efﬁciency can be approximated within few percent as a constant, Qext ≈ 2. The extinction

coefﬁcient becomes independent of the wavelength and can be expressed as the product of the droplet concentration and second radius moment of the DSD:

σext=

_∞

0

2πr2n(r) dr = 2πNr2. (1.10)

Solving forr2_{in Eq. (1.4) and for r}3_{in Eq. (1.5) and substituting both into the deﬁnition}

of the extinction (Eq. 1.10), and collecting constants, it can be expressed as the ratio of the liquid water content to the effective radius:

σ_ext= 2πNr 3 r_e = 3 2 LWC ρ_wr_e. (1.11)

Accordingly the cloud optical depth can be expressed as follows:

τ = _h_ct hcb 3 2 LWC (h) ρ_wr_e(h) dh. (1.12)

The optical depth is an indication of the cloud opacity. For an average value of theLWC

of 0.5 g/m3_{and an effective radius of 12 μm (maritime cloud), the optical extinction is}

ap-proximately 0.07 /m. When the optical extinction is nearly constant throughout the cloud, the cloud optical depth for a 300 m thick layer is 21. As the optical extinction is inversely

pro-portional to the effective radius, the cloud optical depth of the continental cloud (r_e= 6 μm)

is approximately 42 and thus optical denser for the same amount ofLWC . Unless otherwise

speciﬁed, hereinafter the cloud optical depth and optical extinction always refer to the visi-ble range of the electromagnetic spectrum and for droplet radii much larger compared to the wavelength.

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The aircraft in-situ datasets provide very valuable information about the local status of the cloud microphysical properties at very high temporal and spatial resolution. However, aircraft observations in clouds are currently extremely difﬁcult to perform. They are very expensive and never performed on such a continuous basis, that diurnal, seasonal or interan-nual variations in the cloud properties could be examined. The longer-term monitoring of the cloud properties can be gained from satellite or surface remote sensing observations, which supply the fundamental source of information for retrieving the water cloud properties and to characterize their radiative effect (Chaps. 2, 4).

1.3 Surface remote sensing of liquid water clouds

Remote sensing observations of low-level liquid water clouds require high instrumental sen-sitivity and vertical resolution since these clouds are composed of high concentrated and inhomogeneously distributed small liquid water droplets. The synergy of cloud radar, lidar, microwave radiometer and various different radiometric devices provides adequate sensitiv-ity to address such cloud types. Sensor synergy complements the measurement limitation of each individual instrument, which provides more information on the cloud properties of inter-est than observations from one single senor. As mentioned before, such collections of instru-ments are operated at different surface observational sites across the globe, which makes them particularly suitable for the observation of low-level water clouds and the application of the multi-sensor retrieval approaches. For instance, the ARM Program (Ackerman and Stokes, 2003) established three permanent instrumented ﬁeld sites at the Southern Great Plains in Oklahoma, in the Tropical Western Paciﬁc and at the North Slope of Alaska respectively, additionally, ARM has developed and deploys two ARM mobile facilities (AMF). Further-more there are experimental sites located in Europe, for instance Cabauw in The Netherlands, Chilbolton in the United Kingdom, Palaiseau in France and Lindenberg in Germany (Illing-worth et al., 2007). Since 2011, several European ground-based stations have been integrated to the ACTRIS (Aerosols, Clouds, and Trace gases Research InfraStructure) network to pro-vide coordinated and controlled observations of aerosols, clouds, and short-lived gas-phase species from various atmospheric probing instrumentations to ensure long-term monitoring of the atmospheric environment across Europe (ACTRIS, 2011).

In the following, the remote sensing instruments of cloud radar, lidar, microwave ra-diometer and pyranometer are brieﬂy introduced and discussed in the context of liquid wa-ter cloud observations. The key instrument measuring vertical proﬁles of non-precipitating boundary layer clouds is the millimeter-wavelength Doppler radar because of its ability to penetrate clouds. Millimeter-wavelength cloud radars (MMCRs) have been used in atmo-spheric cloud research over the last 30 years and their observations are indispensable for studying the microphysical properties of non-precipitating boundary layer clouds (Kollias et al., 2007). Because of their short wavelengths, they are sensitive to small cloud droplets and ice-crystals so that they detect all types of non-precipitating clouds well before large hy-drometeors are formed. Cloud radars are mainly operating at 35 GHz (8.7 mm wavelength,

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re-lated to water vapor and oxygen reaches a local minimum at these frequencies. The capability of radars to detect the clouds’ hydrometeors depends, in parts, on the concentration and size of the particles present in the radar sample volume, the liquid- or ice water content, the radar wavelength and the sensitivity. Detailed descriptions of cloud observations using completely automated and continuously operating cloud radars and their operating characteristics can be found in Kropﬂi et al. (1995), Clothiaux et al. (1999) and Kollias et al. (2007).

A cloud radar works by transmitting a pulse of millimeter-wave energy, that propagates through the atmosphere until some of the energy is reflected back by the clouds’ hydrom-eteors. The power backscattered from each range sample volume is used to compute the power density spectra together with the first three spectral moments that correspond to the reflectivity Z (dBZ), Doppler velocity (m/s), and Doppler spectral width (m/s). The main measurement quantity of a cloud radar is the cloud hydrometeor reflectivity factor Z, here-after referred to as radar reflectivity. For cloud particles that are small compared to the radar wavelength, the scattering is described by the Rayleigh approximation. The radar reflectivity factor is proportional to the sixth power of the droplet radius:

Z = 26

_∞

0

r6_{n(r) dr = 64Nr}6_, _(1.13)

where Z is in units of mm6/m3. The range of Z can span many orders of magnitude so

that it is transformed for convenience into units of dBZ with 10 log₁₀(Z). Reﬂectivites from

stratus and cirrus to clouds that form drizzle are detectable with good accuracy in the range

of approximately− 50 dBZ to + 20 dBZ up to heights of 10 km above ground level (AGL) or

higher.

Low-level liquid water clouds with droplet sizes of few micrometers have generally small radar reflectivity values. Typical reflectivity profiles of continental and marine boundary layer

stratus increase from the minimum detectable radar reﬂectivity value (≈ − 50 dBZ) at cloud

base up to≈ − 25 dBZ at a certain level above which the reﬂectivity values decrease until

cloud top (Frisch et al., 1995; Fox and Illingworth, 1997). The observed vertical reflectivity profiles provide dependable cloud base and cloud top height estimates (Clothiaux et al., 1995, 2000). Uncertainties in the measurements of the cloud base height can occur by using radar reflectivity values alone, because the lower-altitude radar observations are often contaminated by echoes from non-hydrometeors, such as insects, which have similar reflectivities to those of liquid water clouds. The presence of insects near cloud base would in that case tend to lower the radar-based estimated cloud base height (Clothiaux et al., 2000). In addition, the cloud droplets near cloud base are often very small with reflectivities close to the detection limit of the radar, so that it may fail to detect these particles, which would result in an overes-timation of the radar derived cloud base height (Clothiaux et al., 2000). Furthermore, cloud base can often be masked by the presence of falling drizzle. In these circumstances, col-located observations of laser devices, such as lidar or ceilometer, are more reliable for the estimation of the cloud base height. These instruments operate at wavelengths ranging from the UV (Ultra Violet) to the NIR (Near Infrared), which means that the sizes of particles they are sensitive to are different compared to millimeter-wavelength radars. Ceilometers and

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li-dars are usually insensitive to insects or other non-hydrometeors particularly visible to the radar and they certainly detect layers of small liquid water cloud droplets due to their strong scattering in the UV and VIS (Visible). Therefore they provide more accurate measurements of cloud base heights in the lower altitude. Cloud base heights are commonly measured with zenith-pointing laser ceilometers. They are fully automated systems operating for instance at 905 nm, which produce backscatter proﬁles with a range resolution of 15 m or 30 m to deﬁne the cloud bottom height (Spinhirne et al., 1989). Although the laser devices are more sen-sitive to large concentrations of small particles compared to the cloud radar, the laser beams are rapidly attenuated by liquid water, so that they only provide information at or slightly above the water cloud base. The conjunction of cloud radar and lidar measurements are thus an effective complement to estimate the water cloud boundaries and to discriminate between spherical water droplets and other non-hydrometeors visible to the cloud radar. A detailed description of retrieving water clouds’ vertical extent by cloud radar and lidar observations is given in Boers et al. (2000) and Clothiaux et al. (2000).

In addition to cloud radars and lidars, Microwave radiometer (MWR) observations are widely used in atmospheric research since they provide vertically integrated amounts of cloud

liquid water (LWP) and water vapor (IWV ) in the atmosphere. The standard MWR is a

dual-channel microwave receiver, that measures the emission of the microwave radiation from the atmosphere at two frequencies, namely at 31.4 GHz and at 23.8 GHz. The 31.4 GHz channel is dominated by the emission of liquid water and the 23.8 GHz channel by the emission of atmospheric water vapor. The observed brightness temperatures at these frequencies are

used to retrieve theLWP and the IWV on the basis of statistical retrieval algorithms that

require additional information of atmospheric temperature proﬁles (e.g. Liljegren et al., 2001; L¨ohnert and Crewell, 2003; Turner et al., 2007b). One drawback of these retrieval schemes

is that the estimated accuracy of the retrieved LWPs is approximately 25 g/m2 _{to 30 g/m}2

(Westwater et al., 2001; Crewell and L¨ohnert, 2003), which represents a large uncertainty

for thin low-level boundary clouds with relatively small amounts of LWPs (< 100 g/m2₎

(Turner et al., 2007a). Turner (2007) could improve theLWP accuracy for such cloud types

by considering brightness temperatures offsets observed by the MWR to reduce theLWP

bias in clear sky cases. The improvement of the uncertainty in theLWP is also subject of the

ARM working group called Clouds with Low Optical (Water) depth (CLOWD) (Turner et al., 2007a). They organized the aforementioned ﬁeld campaign RACORO (see list of acronyms and symbols) in 2009 to develop a robust retrieval algorithm that provides among others

accurateLWP for CLOWD-type clouds (Vogelmann et al., 2012).

The simultaneous measurements of surface radiation in addition to the cloud observations are very valuable for studies of surface cloud radiative effects (McFarlane et al., 2008; Ebell et al., 2011). The pyranometer is the primary instrument used to measure the downwelling

broadband shortwave radiative ﬂux density (wavelengths between 0.4 μm and 4 μm) in W/m2

at the ground. It performs hemispherical observations and contains as a radiation receiver a thermopile sensor which is covered by a glass dome. One side of the thermopile’s surface is absorbing the incident radiation on the ground (hot junction) while the other side is shadowed by the pyranometer body (cold junction). The voltage signal produced by the thermopile is

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proportional to the broadband shortwave radiative ﬂux density. Surface broadband radiation measurements of the cloudy sky are used in various ways to infer the cloud optical depth and the effective radius (e.g. Dong et al., 1997; Barnard and Long, 2004; Long and Shi, 2008).

1.4 Background on water cloud multi-sensor retrieval techniques

The liquid water cloud microphysical and optical properties are functions of the droplet size distribution, that are expressed in terms of the moments of the DSD. For instance, the liquid

water content (LWC ) is proportional to the third moment of the DSD, while the optical

extinction (σext) is proportional to the second moment and the effective radius (re) is the

ratio of the third to the second moment of the DSD (Sec. 1.2).

It is useful in cloud remote sensing to characterize the liquid water cloud properties by their moments of the DSD, because the surface remote sensing instruments do not provide di-rect observations of the type of DSD in clouds, but they measure quantities that can be related to the moments of that DSD. For instance, the cloud radar reﬂectivity factor is proportional to the sixth moment of the DSD when the particles are small compared to the radar wavelength (Eq. 1.13). Referring to the lidar or ceilometer devices, the measured backscatter signal can be converted to optical extinction (Klett, 1984), which is proportional to the second moment of the DSD.

The use of certain relationships between the moments of the cloud DSD allows one to link the remote sensing observations to the cloud microphysics. The common liquid water cloud retrieval techniques seek to relate the cloud radar reﬂectivity to the water cloud microphysics since the use of lidars for remote sensing of liquid water clouds is limited because of the rapid attenuation of the signals in liquid water (Sec. 1.3) and the difﬁculty to extract the lidar extinction from the lidar attenuated backscatter signal (Donovan and van Lammeren, 2001). Only few lidar-based methods have been developed in the past (Boers et al., 2000; Martucci and O’Dowd, 2011), while many more attempts have been made to obtain relationships

be-tween the radar reﬂectivity Z and theLWC to propose meaningful parameterizations of the

LWC for non-drizzling liquid water clouds (e.g. Atlas, 1954; Sauvageot and Omar, 1987;

Liao and Sassen, 1994; Fox and Illingworth, 1997; Wang and Geerts, 2003). These

algo-rithms generally assume a power-law relationship, Z = aLWCb, with ﬁtting coefﬁcients a

and b either obtained from co-locatedLWC aircraft in-situ observations (e.g. Fox and

Illing-worth, 1997; Wang and Geerts, 2003) or from an adiabatic cloud model (Liao and Sassen, 1994; Sassen and Liao, 1996). The coefﬁcients a and b found in these studies are in the same

order of magnitude, but a unique relationship between the radar reﬂectivity and theLWC

does not exist due to the prevailing differences between the microphysical characteristics that are observed in various water cloud types (Khain et al., 2008).

Several water cloud retrieval methods use the radar reﬂectivity factor in combination with

observations from different passive instruments, such as theLWP from the MWR (White

et al., 1991; Frisch et al., 1995, 1998, 2000, 2002; Sassen et al., 1999) and the solar trans-mission (Dong et al., 1998; Mace and Sassen, 2000; Dong and Mace, 2003). These methods generally assume a DSD function, like gamma or log-normal, with a ﬁxed DSD shape or

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width chosen from in-situ data to obtain a theoretical relation between the radar reﬂectivity

and theLWC (Chap. 2). They retrieve then the LWC from the reﬂectivity proﬁle by

integrat-ing theLWC and matching it with the LWP obtained by the MWR (White et al., 1991; Frisch

et al., 1995; Sassen et al., 1999). The use of certain combinations of the DSD moments or em-pirical assumptions on the droplet concentration enable furthermore to retrieve the effective radius. The methods of Mace and Sassen (2000) and Dong and Mace (2003) are additionally constrained to match the solar transmission at the surface. The multi-sensor approaches are

generally more robust compared to the radar reﬂectivity-only retrievals of theLWC

(Ma-trosov et al., 2004), but on the other hand in many retrievals the uncertainty depends on the assumed DSD and its width or shape parameter. There are signiﬁcant errors in the retrievals when the assumed DSD parameters deviate from the actual cloud DSD (Miles et al., 2000).

In this context, Kato et al. (2001) proposed a technique to estimate theLWC and effective

radius from a statistical model that relates the particles sizes to the Doppler velocity obtained by the cloud radar to reduce the error on the assumed DSD width or shape parameters. How-ever, the applications of all radar reﬂectivity-based methods mentioned above are limited to liquid water clouds without the presence of drizzle drops. If drizzle drops are present in the radar resolution volume, they contribute substantially to the radar reﬂectivity factor as they

behave as Rayleigh scatters∝ r6. Since the concentration of drizzle drops is rather low

compared to the concentration of the smaller droplets, their contribution to theLWC is

neg-ligible small. Therefore, the empirical and theoretical obtained Z-LWC relationships will

produce biased results when applied to drizzling liquid water clouds.

More advanced methods, that are also applicable to drizzling clouds, have been developed by McFarlane et al. (2002) and Löhnert et al. (2004, 2007, 2008). The method of McFarlane et al. (2002) is based on Bayes’ theorem of conditional probability which combines MWR brightness temperature, cloud radar reflectivity and radiosonde profiles of temperature and humidity with prior information on the moments of DSDs from in-situ data sets of non-drizzling and non-drizzling clouds to obtain the cloud microphysics. The technique of Löhnert

et al. (2004, 2007, 2008) retrieves proﬁles of temperature, humidity andLWC from the

re-mote sensing measurements on the basis of optimal estimation equations. The so-called In-tegrated Proﬁling Technique (IPT) incorporates the target classiﬁcation scheme from Hogan and O’Connor (2004), that discriminates between different hydrometeor categories, such as

drizzle and cloud water droplets only, to integrate theLWC proﬁling method of Krasnov and

Russchenberg (2002, 2006). This enables the applicability of the technique also on a routinely basis to produce long-term liquid water cloud microphysical retrievals (Ebell et al., 2010).

The continuous estimates of the cloud microphysical properties are particularly needed for climate studies and for the evaluation of the satellite observations. For this purpose, the ARM Climate Research Facility provides a Continuous Baseline Microphysical Retrieval (MICROBASE) value-added product (VAP) (Miller et al., 2003; Dunn et al., 2011).

MICROBASE runs operationally and estimates the cloud microphysical properties at all ARM ﬁxed sites for liquid water and ice clouds. It combines various ground-based remote

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sensing observations with accepted retrieval algorithms, such as the one of Liao and Sassen (1994) for water clouds. Also the Cloudnet project (Illingworth et al., 2007) provides con-tinuous estimates among others of the vertical proﬁles of the cloud ice and the liquid water contents for various observational sites in Europe and for the ones operated by ARM.

Considering the increase in the number of cloud property remote sensing techniques, that are only partly mentioned above, more research attention is currently focused on the inter-comparison of the various cloud property retrievals (e.g. L¨ohnert et al., 2003; Turner et al., 2007a; Comstock et al., 2007; Zhao et al., 2011, 2012; Huang et al., 2012; Mather et al., 2012). A comparison of a number of different cloud property retrieval algorithms, including MICROBASE, Cloudnet, Dong and Mace (2003), found large systematic differences between

the liquid water cloud retrievals of theLWC and the effective radius, particularly in terms

of their vertical distributions although partly the same observations are used as inputs. This is the result of the differences in the retrieval theory basis. Similar results are also shown in

the study of Huang et al. (2012) in which theLWC retrievals of four radar-based retrieval

techniques disagree in their vertical distributions, especially at the cloud boundaries. Also the study of L¨ohnert et al. (2003) demonstrated differences between the vertical proﬁles of

theLWC retrievals of the mentioned IPT technique and of a simpler radar-based technique

(Frisch et al., 1998).

In order to understand the cloud property retrieval differences and uncertainties and to further improve and optimize the retrieval products, there are a number of research projects that address this issue, like the QUICR (Quantification of Uncertainties in Cloud Retrieval) project (Zhao et al., 2011) in which the cloud retrieval products from nine algorithms (e.g., MICROBASE, Cloudnet, Dong and Mace (2003)) at five ARM permanent research sites are assembled into one data set (ACRED, ARM Cloud Retrieval Ensemble Data Set). This pro-vides very valuable information on the uncertainties in the products in terms of long-term statistical inter-comparisons which quantify the spread between the algorithms’ outputs. As a further step, the determination of which algorithm produces more accurate cloud property retrievals requires the use of independent validation approaches. For instance, McFarlane et al. (2008) and Ebell et al. (2011) performed shortwave and longwave radiative closure studies, in which the cloud property retrievals are evaluated in terms of being able to repro-duce the measured surface and top-of-atmosphere radiative fluxes. Other studies, like Löhnert et al. (2007) evaluated the outputs from various retrieval techniques by using synthetic ob-servations of modeled clouds as inputs to retrieve the cloud properties in order to quantify the uncertainties directly by comparing the retrieval products with the ones of the modeled clouds. Moreover, the accuracy can be evaluated by comparing the cloud retrievals with air-craft in-situ data, that is one of the experimental goal of the ARM working group CLOWD and the aforementioned organized field campaign RACORO (Vogelmann et al., 2012). Cur-rently opportunities for collaboration between European centers and ARM are being explored to establish a Trans-Atlantic working group in order to identify outstanding climate change science questions and to define observational strategies to address them (Mather et al., 2012). One activity is focused on the inter-comparison of ground-based retrieval techniques and to define a common approach for evaluating the uncertainties in cloud property retrievals.

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1.5 This thesis

Using the synergy of surface remote sensing observations to obtain vertically resolved liquid water cloud properties provides the ability to generate long-term cloud property data sets with high temporal and vertical resolution for different surface remote sensing stations across the globe. The monitoring of cloud property variability on a local scale can be used to evaluate satellite-based retrieved cloud properties and to assist the improvement of cloud parameter-izations in weather and climate models. This requires robust cloud property retrievals of sufﬁcient accuracy, which can serve as references for further validation studies. Although a number of advanced and quality liquid water cloud property retrieval techniques exist, the

results of various inter-comparison studies of cloud property retrievals (LWC and effective

radius) based on current surface remote sensing techniques demonstrate large differences be-tween the cloud properties, in particular in terms of their vertical distributions (L¨ohnert, 2002; L¨ohnert et al., 2003; Zhao et al., 2012; Huang et al., 2012). This is related to the differences in the used retrieval theories, assumptions, retrieval inputs and constrains.

As a result of the diversity in the vertically resolved cloud property retrievals from the surface-remote sensing techniques and the difﬁculties in characterizing the vertical cloud structure from passive satellite remote sensing, the work presented here aims to provide a better understanding of the assumptions that are commonly used to parametrize the in-cloud vertical structure by exploring the impact of the different assumptions on the computations of the cloud property retrievals. To address this objective the ﬁrst part of the thesis presents three retrieval approaches that rely on different vertical cloud models, which require as inputs the

cloud radar reﬂectivity; the microwave radiometer estimatedLWP and the cloud geometrical

thickness from lidar and cloud radar. The vertical cloud models are widely used in satellite and ground-based retrievals schemes. In the ﬁrst model, the vertical structure of the cloud is parameterized as being vertically homogeneous, the second vertical cloud model originates from an adiabatic model and third one considers the impact of cloud top entrainment mixing processes on the microphysical properties. The natures of the ﬁrst two models mentioned are rather simple and they are often applied when the remote sensing observations provide little or uncertain information on the inner-cloud vertical structure, such as spectral radiance

ob-servations from space-based radiometers or the microwave radiometer product of theLWP.

The latter mentioned model presents a standard theoretical framework for cloud radar-based retrieval techniques, which relies on the profile of the measured radar reflectivity. Integrating the mentioned remote sensing observations and a priori assumptions about the droplet size distribution into these vertical cloud models, the following cloud properties as functions of the cloud height are obtained: (1) the liquid water content, (2) the droplet concentration, (3) the effective radius, (4) the visible optical extinction and the (5) optical depth. The advantage of using the same observations as input is that the cloud property retrievals of the vertical cloud models can be related to each other and it allows the quantification of the effects of the different model assumptions on the cloud property retrievals. This provides the ability to understand the impact of the simpler models on the computations of the microphysical parameters especially when information about the vertical structure is available. The

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dif-ferences between the cloud property retrievals are explored on the basis of remote sensing observations that appear to be typical for low-level water clouds, and the sensitivity of the retrievals to the input observations and to the model assumptions are evaluated. In contrast

to other studies, that predominantly compare the retrievals of theLWC and the effective

ra-dius, this work also considers the inter-comparison of the droplet concentration, the optical extinction and the optical depth retrievals, that are the key parameters involved in the indirect aerosol effects.

As a further step, the second part of the thesis focuses on evaluating the different cloud models by determining the validity of the cloud property retrievals on the basis of two in-dependent validation approaches. First, the reliability of the cloud property retrievals are evaluated on the basis of synthetic surface remote sensing observations of a modeled liquid water cloud layer. The microphysical retrieval that results from using the synthetic obser-vations as input for the three algorithms are compared with the properties of the modeled cloud to quantify the accuracy of liquid water cloud retrieval algorithms. Further, the cloud microphysical properties obtained from real observations of a water cloud layer are evalu-ated in terms of a shortwave radiation closure analysis. Both validation methods complement each individual approach, so that conclusions on the validity of the vertical distribution of the cloud properties and on the extent to which they affect the solar radiation can be drawn.

This thesis is organized as follows:

Chap. 2 presents three algorithms for the retrieval of liquid water cloud properties on the ba-sis of different vertical cloud models. The different cloud models are referred to as the vertical uniform (VU) cloud model, the scaled-adiabatic stratiﬁed (SAS) cloud model and the radar reﬂectivity-homogeneous mixing (HM) cloud model. These algorithms retrieve cloud properties from a single set of ground-based remote sensing observations

of the cloud radar reﬂectivity, the microwave radiometer observedLWP and the cloud

geometrical thickness from lidar and cloud radar. The differences between the cloud property retrievals using the three approaches are determined, and the sensitivity of the retrievals to the input observations and to the model assumptions are evaluated. Chap. 3 evaluates the liquid water cloud property retrievals from the three vertical models

on the basis of synthetic remote sensing observations of a modeled liquid water cloud layer, that are generated by the EarthCARE Simulator (ECSIM) (Donovan et al., 2004). The accuracy and precision of the cloud properties based on the three algorithms are assessed by comparing the retrievals from the synthetic observations with the modeled liquid water cloud properties in dependence upon the validity of the used vertical cloud model, the model assumptions and upon the uncertainty of the algorithms inputs, such as the cloud radar reﬂectivity measurements and the microwave radiometer estimated liquid water path.

Chap. 4 evaluates the liquid water cloud property retrievals based on the three algorithms by means of a shortwave radiation closure experiment of a single-layer stratocumulus cloud layer. The retrieved cloud properties are used as inputs for the radiation transfer

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model in ECSIM to simulate the surface broadband shortwave radiation. Ground-based solar radiation observations are used to evaluate these simulations. In addition, sensi-tivity studies are performed to demonstrate the impact of the vertical distribution of the cloud properties on the broadband shortwave radiation

Chap. 5 summarizes the main results of the study and presents an outlook on future devel-opments and applications.

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Retrieval of liquid water cloud properties from ground-based remote sensing observations

Retrieval of liquid water cloud

properties

from ground-based remote sensing

observations

cloud pr

operties

Christine Knist

Christine Knist

Invitation

You are kindly

invited to attend my

Ph.D. defense

entitled:

Retrieval of liquid

water cloud

properties from

ground-based

remote sensing

observations

On Monday

September 29, 2014

at 10:00 in the

Senaatszaal of the

Auditorium

at the Delft

University of

Technology

Before the defense

from 9:30 till 9:45,

I will give a short

talk about the

contents of the thesis.

A reception will take

place after the

defense.

Christine Knist

c.l.knist@tudelft.nl

ground-based remote sensing observations

Proefschrift

Christine Luise KNIST

Abstract

Samenvatting

Contents

Chapter 1

Introduction

1.1

Motivation

1.2

Liquid water cloud microphysical and optical characteristics

1.3

Surface remote sensing of liquid water clouds

1.4

Background on water cloud multi-sensor retrieval techniques

1.5

This thesis