Robin J. Hogan

Cloudnet: Continuous Evaluation of Cloud Profiles in Seven Operational Models Using Ground-Based Observations

Cloud fraction, liquid and ice water contents derived from long-term radar, lidar and microwave radiometer data are systematically compared to models to quantify and improve their performance.

Combined CloudSat-CALIPSO-MODIS retrievals of the properties of ice clouds

[1] In this paper, data from spaceborne radar, lidar and infrared radiometers on the “A‐Train” of satellites are combined in a variational algorithm to retrieve ice cloud properties. The method allows a seamless retrieval between regions where both radar and lidar are sensitive to the regions where one detects the cloud. We first implement a cloud phase identification method, including identification of supercooled water layers using the lidar signal and temperature to discriminate ice from liquid. We also include rigorous calculation of errors assigned in the variational scheme. We estimate the impact of the microphysical assumptions on the algorithm when radiances are not assimilated by evaluating the impact of the change in the area‐diameter and the density‐diameter relationships in the retrieval of cloud properties. We show that changes to these assumptions affect the radar‐only and lidar‐only retrieval more than the radar‐lidar retrieval, although the lidar‐only extinction retrieval is only weakly affected. We also show that making use of the molecular lidar signal beyond the cloud as a constraint on optical depth, when ice clouds are sufficiently thin to allow the lidar signal to penetrate them entirely, improves the retrieved extinction. When infrared radiances are available, they provide an extra constraint and allow the extinction‐to‐backscatter ratio to vary linearly with height instead of being constant, which improves the vertical distribution of retrieved cloud properties.

A variational scheme for retrieving ice cloud properties from combined radar, lidar, and infrared radiometer

[1] A variational method is described for retrieving profiles of visible extinction coefficient, ice water content and effective radius in ice clouds using the combination of ground-based or spaceborne radar, lidar and infrared radiometer. The forward model includes effects such as non-Rayleigh scattering by the radar and molecular and multiple scattering by the lidar. By rigorous treatment of errors and a careful choice of state variables and associated a priori estimates, a seamless retrieval is possible between regions of the cloud detected by both radar and lidar and regions detected by just one of these two instruments. Thus, when the lidar signal is unavailable (for reasons such as strong attenuation), the retrieval tends toward an empirical relationship using radar reflectivity factor and temperature, and when the radar signal is unavailable (such as in optically thin cirrus), accurate retrievals are still possible from the combination of lidar and radiometer. The method is tested first on simulated profiles from aircraft data and then on real observations taken in West Africa. It would be straightforward to expand the approach to include other measurements simply by including a forward model for them.

The Retrieval of Ice Water Content from Radar Reflectivity Factor and Temperature and Its Use in Evaluating a Mesoscale Model

Abstract Ice clouds are an important yet largely unvalidated component of weather forecasting and climate models, but radar offers the potential to provide the necessary data to evaluate them. First in this paper, coordinated aircraft in situ measurements and scans by a 3-GHz radar are presented, demonstrating that, for stratiform midlatitude ice clouds, radar reflectivity in the Rayleigh-scattering regime may be reliably calculated from aircraft size spectra if the “Brown and Francis” mass–size relationship is used. The comparisons spanned radar reflectivity values from −15 to +20 dBZ, ice water contents (IWCs) from 0.01 to 0.4 g m−3, and median volumetric diameters between 0.2 and 3 mm. In mixed-phase conditions the agreement is much poorer because of the higher-density ice particles present. A large midlatitude aircraft dataset is then used to derive expressions that relate radar reflectivity and temperature to ice water content and visible extinction coefficient. The analysis is an advance over previo...

The EarthCARE Satellite: The Next Step Forward in Global Measurements of Clouds, Aerosols, Precipitation, and Radiation

AbstractThe collective representation within global models of aerosol, cloud, precipitation, and their radiative properties remains unsatisfactory. They constitute the largest source of uncertainty in predictions of climatic change and hamper the ability of numerical weather prediction models to forecast high-impact weather events. The joint European Space Agency (ESA)–Japan Aerospace Exploration Agency (JAXA) Earth Clouds, Aerosol and Radiation Explorer (EarthCARE) satellite mission, scheduled for launch in 2018, will help to resolve these weaknesses by providing global profiles of cloud, aerosol, precipitation, and associated radiative properties inferred from a combination of measurements made by its collocated active and passive sensors. EarthCARE will improve our understanding of cloud and aerosol processes by extending the invaluable dataset acquired by the A-Train satellites CloudSat, Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), and Aqua. Specifically, EarthCARE’s c...

Toward Understanding of Differences in Current Cloud Retrievals of ARM Ground-Based Measurements

Accurate observations of cloud microphysical properties are needed for evaluating and improving the representation of cloud processes in climate models and better estimate of the Earth radiative budget. However, large differences are found in current cloud products retrieved from ground-based remote sensing measurements using various retrieval algorithms. Understanding the differences is an important step to address uncertainties in the cloud retrievals. In this study, an in-depth analysis of nine existing ground-based cloud retrievals using ARM remote sensing measurements is carried out. We place emphasis on boundary layer overcast clouds and high level ice clouds, which are the focus of many current retrieval development efforts due to their radiative importance and relatively simple structure. Large systematic discrepancies in cloud microphysical properties are found in these two types of clouds among the nine cloud retrieval products, particularly for the cloud liquid and ice particle effective radius. Note that the differences among some retrieval products are even larger than the prescribed uncertainties reported by the retrieval algorithm developers. It is shown that most of these large differences have their roots in the retrieval theoretical bases, assumptions, as well as input and constraint parameters. This study suggests the need to further validate current retrieval theories and assumptions and even the development of new retrieval algorithms with more observations under different cloud regimes.

Comparison of ECMWF Winter-Season Cloud Fraction with Radar-Derived Values

Abstract Of great importance for the simulation of climate using general circulation models is their ability to represent accurately the vertical distribution of fractional cloud amount. In this paper, a technique to derive cloud fraction as a function of height using ground-based radar and lidar is described. The relatively unattenuated radar detects clouds and precipitation throughout the whole depth of the troposphere, whereas the lidar is able to locate cloud base accurately in the presence of rain or drizzle. From a direct comparison of 3 months of cloud fraction observed at Chilbolton, England, with the values held at the nearest grid box of the European Centre for Medium-Range Forecasts (ECMWF) model it is found that, on average, the model tends to underpredict cloud fraction below 7 km and considerably overpredict it above. The difference below 7 km can in large part be explained by the fact that the model treats snow and ice cloud separately, with snow not contributing to cloud fraction. Modifyin...

A Technique for Autocalibration of Cloud Lidar

Abstract In this paper a technique for autocalibration of a cloud lidar is demonstrated. It is shown that the lidar extinction-to-backscatter ratio derived from integrated backscatter for stratocumulus is, in the absence of drizzle, constrained to a theoretical value of 18.8 ± 0.8 sr at a wavelength of 905 nm. The lidar can be calibrated by scaling the backscatter signal so that the observed lidar ratio matches the theoretical value when suitable conditions of stratocumulus are available. For a beam divergence of 1–1.5 mrad, multiple scattering introduces an uncertainty of about 10% into the calibration and for a narrow-beam ground-based lidar, with negligible multiple scattering, calibration may be possible to better than 5%. Some examples of the mean lidar ratio of supercooled liquid water layers and ice clouds inferred using this technique are also shown.

Properties of embedded convection in warm-frontal mixed-phase cloud from aircraft and polarimetric radar

Paper associated with the CWVC (Clouds, Water Vapour and Climate) dataset held on the CEDA archive.

Radar Scattering from Ice Aggregates Using the Horizontally Aligned Oblate Spheroid Approximation

AbstractThe assumed relationship between ice particle mass and size is profoundly important in radar retrievals of ice clouds, but, for millimeter-wave radars, shape and preferred orientation are important as well. In this paper the authors first examine the consequences of the fact that the widely used “Brown and Francis” mass–size relationship has often been applied to maximum particle dimension observed by aircraft Dmax rather than to the mean of the particle dimensions in two orthogonal directions Dmean, which was originally used by Brown and Francis. Analysis of particle images reveals that Dmax ≃ 1.25Dmean, and therefore, for clouds for which this mass–size relationship holds, the consequences are overestimates of ice water content by around 53% and of Rayleigh-scattering radar reflectivity factor by 3.7 dB. Simultaneous radar and aircraft measurements demonstrate that much better agreement in reflectivity factor is provided by using this mass–size relationship with Dmean. The authors then examine t...