Anthony J. Illingworth

THE CLOUDSAT MISSION AND THE A-TRAIN A New Dimension of Space-Based Observations of Clouds and Precipitation

CloudSat is a satellite experiment designed to measure the vertical structure of clouds from space. The expected launch of CloudSat is planned for 2004, and once launched, CloudSat will orbit in formation as part of a constellation of satellites (the A-Train) that includes NASAs Aqua and Aura satellites, a NASA–CNES lidar satellite (CALIPSO), and a CNES satellite carrying a polarimeter (PARASOL). A unique feature that CloudSat brings to this constellation is the ability to fly a precise orbit enabling the fields of view of the CloudSat radar to be overlapped with the CALIPSO lidar footprint and the other measurements of the constellation. The precision and near simultaneity of this overlap creates a unique multisatellite observing system for studying the atmospheric processes essential to the hydrological cycle. The vertical profiles of cloud properties provided by CloudSat on the global scale fill a critical gap in the investigation of feedback mechanisms linking clouds to climate. Measuring these profi...

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.

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...

Toward More Accurate Retrievals of Ice Water Content from Radar Measurements of Clouds

There has been considerable discussion concerning the accuracy of values of ice water content (IWC) in ice clouds derived from measurements of radar reflectivity ( Z). In this paper, the various published relationships that are based on ice particle size spectra recorded from aircraft are analyzed, and it is shown that a relationship between ice water content and reflectivity can be derived (IWC 5 0.137Z 0.64 at 94 GHz and IWC 5 0.097Z 0.59 at 35 GHz), which only varies by 20%‐30% for different climatological areas, providing the same ice density as a function of particle size is assumed. Uncertainty as to the true variation of density of ice particles with size may reduce the average IWC for a given Z by up to 30% for an IWC of 0.1 gm 23 and 20% for an IWC of 0.01 g m23. Individual values of IWC derived from a single measurement of Z are likely to have an error of about 1100% and 250%, but if some characteristic size estimate is available, this is reduced to about 150% and 230%. The remaining errors are due to deviations of the size spectra from exponentiality, so there is no advantage in measuring the characteristic size more precisely than this limit. Remote sensing of ice particle size is not trivial, and it is shown that if instead of size, an estimate of the temperature of the ice cloud to within 6 K is available, then, rather surprisingly, the reduction in the error of IWC is almost as good as that achieved using size. Essentially this result is reflecting the well-known correlation of crystal size with temperature. When the mean values of IWC for a given Z and T are compared for a tropical and midlatitude dataset using a common ice density variation with size, then the difference is usually less than 25%. A spaceborne instrument may need to integrate over horizontal distances of 10 km to achieve sufficient sensitivity; this necessity may introduce a bias into the retrieved IWC because the relationship between IWC and Z is not linear, but analysis shows that any bias should be less than 10%.

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...

The Need to Represent Raindrop Size Spectra as Normalized Gamma Distributions for the Interpretation of Polarization Radar Observations

Polarization radar techniques essentially rely on detecting the oblateness of raindrops to provide a measure of mean raindrop size and then using this information to give a better estimate of rainfall rate R than is available from radar reflectivity Z alone. To derive rainfall rates from these new parameters such as differential reflectivity ZDR and specific differential phase shift KDP and to gauge their performance, it is necessary to know the range of naturally occurring raindrop size spectra. A three parameter gamma function is in widespread use, with the three variables No, Do, and m providing a measure of drop concentration, mean size, and spectral shape, respectively. It has become standard practice to derive the range of these three variables in rain by comparing the 69 published values of the constants a and b in the empirical relationships Z 5 aRb with the values of a and b obtained when R and Z are derived by integrating the appropriately weighted gamma function. The relationships in common use both for inferring R from Z, ZDR, and KDP, and for developing attenuation correction routines have been derived from a best fit through the values obtained by cycling over these predicted ranges of No, Do, and m. It is pointed out that this derivation of the predicted range of No, Do, and m arises using a flawed logic for a particular nonnormalized form of the gamma function, and it is shown that the predicted ranges give rise to some very unrealistic drop spectra, including many with high rainfall and very small drop sizes. It is suggested that attenuation correction routines relying on differential phase may be suspect and the commonly used relationships between rainfall rate and Z, ZDR, and KDP need to be reexamined. When more realistic drop shapes are also used, it may be that published relationships for deriving R from Z and ZDR are in error by over a factor of 2; a new equation is proposed that, in the absence of hail and attenuation, should yield values of R accurate to 25%, provided that ZDR can be estimated to 0.2 dB and Z is calibrated to 1 dB. Relationships of the form R 5 a , with b 5 1.15, are in widespread use, but more realistic drop spectra and drop shapes b KDP yield a value of b closer to 1.4, similar to the exponent in Z‐R relationships. In accord, although KDP has the advantage of insensitivity to hail, it may have the same sensitivity to variations in drop spectra as Z does. In addition, the higher value of the exponent b implies that the proposed use of the total phase shift to give the path-integrated total rainfall is also questionable. However, the consistency of Z, ZDR, and KDP in rain can be used to provide absolute calibration of Z to 0.5 dB (12%), and when it fails it indicates that hail is present, in which case a relationship of the form KDP 5 aR1.4 should be used. The technique should work at S, C, and X band, but, in all cases, paths should be chosen so that the total phase shift is not large enough to introduce significant attenuation of Z and ZDR.

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.

Retrieving Stratocumulus Drizzle Parameters Using Doppler Radar and Lidar

Abstract Stratocumulus is one of the most common cloud types globally, with a profound effect on the earth’s radiation budget, and the drizzle process is fundamental in understanding the evolution of these boundary layer clouds. In this paper a combination of 94-GHz Doppler radar and backscatter lidar is used to investigate the microphysical properties of drizzle falling below the base of stratocumulus clouds. The ratio of the radar to lidar backscatter power is proportional to the fourth power of mean size, and so potentially it can provide an accurate size estimate. Information about the shape of the drop size distribution is then inferred from the Doppler spectral width. The algorithm estimates vertical profiles of drizzle parameters such as liquid water content, liquid water flux, and vertical air velocity, assuming that the drizzle size spectrum may be represented by a gamma distribution. The depletion time scale of cloud liquid water through the drizzle process can be estimated when the liquid water...

A Method for Estimating the Turbulent Kinetic Energy Dissipation Rate from a Vertically Pointing Doppler Lidar, and Independent Evaluation from Balloon-Borne In Situ Measurements

Abstract A method of estimating dissipation rates from a vertically pointing Doppler lidar with high temporal and spatial resolution has been evaluated by comparison with independent measurements derived from a balloon-borne sonic anemometer. This method utilizes the variance of the mean Doppler velocity from a number of sequential samples and requires an estimate of the horizontal wind speed. The noise contribution to the variance can be estimated from the observed signal-to-noise ratio and removed where appropriate. The relative size of the noise variance to the observed variance provides a measure of the confidence in the retrieval. Comparison with in situ dissipation rates derived from the balloon-borne sonic anemometer reveal that this particular Doppler lidar is capable of retrieving dissipation rates over a range of at least three orders of magnitude. This method is most suitable for retrieval of dissipation rates within the convective well-mixed boundary layer where the scales of motion that the D...