Characterization of clouds and their radiative effects using ground-based instrumentation at a low-mountain site

The interaction of clouds with radiation and aerosols is the greatest source of uncertainty in future climate projections. Part of the reason is the limited amount of observations of clouds and hence the limited knowledge of cloud macro- and microphysical statistics in connection to their effects on the radiative budget and on the vertical redistribution of energy within the atmosphere. In 2007, the Atmospheric Radiation Measurement program�s (ARM) Mobile Facility (AMF) was operated for a nine-month period in the Murg Valley, Black Forest, Germany, in support of the Convective and Orographically-induced Precipitation Study (COPS). Based on the measurements of the AMF and COPS partner instrumentation, the present study aims at improving the data basis of cloud macro- and microphysical statistics and to assess the potential of the derived cloud properties to estimate the radiative effects of clouds. The synergy of various instruments is exploited to derive a data set of high quality thermodynamic and cloud property profiles with a temporal resolution of 30 s. While quality filters in the cloud microphysical retrieval techniques mostly affect the representativity of ice and mixed clouds in the data sample, water clouds are very well represented in the derived 364,850 atmospheric profiles. In total, clouds are present 72% of the time with multi-layer mixed phase (28.4%) and single-layer water clouds (11.3%) occurring most frequently. In order to evaluate the derived thermodynamic and cloud property profiles,radiative closure studies are performed with independent radiation measurements. In clear sky, average differences between calculated and observed surface fluxes are less than 2.1% and 3.6% for the shortwave and longwave, respectively. In cloudy situations, differences, in particular in the shortwave, are much larger, but most of these can be related to broken cloud situations. The cloud radiative effect (CRE), i.e. the difference of cloudy and clear-sky net fluxes, has been analyzed for the whole nine-month period. The largest surface (SFC) net CRE has been found for multi-layer water (-110 Wm-2) and mixed clouds (-116 Wm-2). The estimated uncertainties in the modeled SFC and top of atmopshere (TOA) net CRE are up to 39% and 26%, respectively. For overcast, single-layer water clouds, sensitivity studies reveal that the SW CRE uncertainty at the SFC and TOA is likewise determined by uncertainties in liquid water path (LWP) and effective radius, if the LWP is larger than 100 gm-2. For low LWP values, uncertainties in SFC and TOA shortwave CRE are dominated by the uncertainty in LWP. Uncertainties in CRE due to uncertainties in the shape of the liquid water content (LWC) profile are typically smaller by a factor of two compared to LWP uncertainties. For the difference between the cloudy and clear-sky net heating rates, i.e. the cloud radiative forcing (CRF), of water clouds, the LWP and its vertical distribution within the cloud boundaries are the most important factors. In order to increase the accuracy of LWC profiles and consequentially of the estimates of CRE and CRF, advanced LWC retrieval techniques, such as the Integrated Profiling Technique (IPT), are needed. The accuracy of a LWC profile retrieval using typical microwave radiometer brightness temperatures and/or cloud radar reflectivities is investigated for two realistic cloud profiles. The interplay of the errors of the a priori profile, measurements and forward model on the retrieved LWC error and on the information content of the measurements is analyzed in detail. It is shown that the inclusion of the microwave radiometer observations in the LWC retrieval increases the number of degrees of freedom, i.e. the independent pieces of information in the measurements, by about 1 compared to a retrieval using measuremets from the cloud radar alone. Assuming realistic measurement and forward model errors, it is further demonstrated, that the error in the retrieved LWC is 60% or larger, if no a priori information is available, and that a priori information is essential for a better accuracy. The results of the present work strongly suggest to improve the LWC a priori profile and the corresponding error estimates in the IPT. However, there are few observational datasets available to construct accurate a priori profiles of LWC, and thus more observational data are needed to improve the knowledge of the a priori profile and the corresponding error covariance matrix.