Equation for the microwave backscatter cross section of aggregate snowflakes using the self-similar Rayleigh–Gans approximation

In this paper an equation is derived for the mean backscatter cross section of an ensemble of snowflakes at centimeter and millimeter wavelengths. It uses the Rayleigh–Gans approximation, which has previously been found to be applicable at these wavelengths due to the low density of snow aggregates. Although the internal structure of an individual snowflake is random and unpredictable, the authors find from simulations of the aggregation process that their structure is “self-similar” and can be described by a power law. This enables an analytic expression to be derived for the backscatter cross section of an ensemble of particles as a function of their maximum dimension in the direction of propagation of the radiation, the volume of ice they contain, a variable describing their mean shape, and two variables describing the shape of the power spectrum. The exponent of the power law is found to be −. In the case of 1-cm snowflakes observed by a 3.2-mm-wavelength radar, the backscatter is 40–100 times larger than that of a homogeneous ice–air spheroid with the same mass, size, and aspect ratio.

Direct observations of the full Dungey convection cycle in the polar ionosphere for southward interplanetary magnetic field conditions

Tracking the formation and full evolution of polar cap ionization patches in the polar ionosphere, we directly observe the full Dungey convection cycle for southward interplanetary magnetic field (IMF) conditions. This enables us to study how the Dungey cycle influences the patches’ evolution. The patches were initially segmented from the dayside storm enhanced density plume at the equatorward edge of the cusp, by the expansion and contraction of the polar cap boundary due to pulsed dayside magnetopause reconnection, as indicated by in situ Time History of Events and Macroscale Interactions during Substorms(THEMIS) observations. Convection led to the patches entering the polar cap and being transported antisunward, while being continuously monitored by the globally distributed arrays of GPS receivers and Super Dual Auroral Radar Network radars. Changes in convection over time resulted in the patches following a range of trajectories, each of which differed somewhat from the classical twin-cell convection streamlines. Pulsed nightside reconnection, occurring as part of the magnetospheric substorm cycle, modulated the exit of the patches from the polar cap, as confirmed by coordinated observations of the magnetometer at Tromsø and European Incoherent Scatter Tromsø UHF radar. After exiting the polar cap, the patches broke up into a number of plasma blobs and returned sunward in the auroral return flow of the dawn and/or dusk convection cell. The full circulation time was about 3 h.

SPARE-ICE: synergistic Ice Water Path from passive operational sensors

This article presents SPARE-ICE, the Synergistic Passive Atmospheric Retrieval Experiment-ICE. SPARE-ICE is the first Ice Water Path (IWP) product combining infrared and microwave radiances. By using only passive operational sensors, the SPARE-ICE retrieval can be used to process data from at least the NOAA 15 to 19 and MetOp satellites, obtaining time series from 1998 onward. The retrieval is developed using collocations between passive operational sensors (solar, terrestrial infrared, microwave), the CloudSat radar, and the CALIPSO lidar. The collocations form a retrieval database matching measurements from passive sensors against the existing active combined radar-lidar product 2C-ICE. With this retrieval database, we train a pair of artificial neural networks to detect clouds and retrieve IWP. When considering solar, terrestrial infrared, and microwave-based measurements, we show that any combination of two techniques performs better than either single-technique retrieval. We choose not to include solar reflectances in SPARE-ICE, because the improvement is small, and so that SPARE-ICE can be retrieved both daytime and nighttime. The median fractional error between SPARE-ICE and 2C-ICE is around a factor 2, a figure similar to the random error between 2C-ICE ice water content (IWC) and in situ measurements. A comparison of SPARE-ICE with Moderate Resolution Imaging Spectroradiometer (MODIS), Pathfinder Atmospheric Extended (PATMOS-X), and Microwave Surface and Precipitation Products System (MSPPS) indicates that SPARE-ICE appears to perform well even in difficult conditions. SPARE-ICE is available for public use.

Systematic and random errors between collocated satellite ice water path observations

There remains large disagreement between ice-water path (IWP) in observational data sets, largely because the sensors observe different parts of the ice particle size distribution. A detailed comparison of retrieved IWP from satellite observations in the Tropics (!30 " latitude) in 2007 was made using collocated measurements. The radio detection and ranging(radar)/light detection and ranging (lidar) (DARDAR) IWP data set, based on combined radar/lidar measurements, is used as a reference because it provides arguably the best estimate of the total column IWP. For each data set, usable IWP dynamic ranges are inferred from this comparison. IWP retrievals based on solar reflectance measurements, in the moderate resolution imaging spectroradiometer (MODIS), advanced very high resolution radiometer–based Climate Monitoring Satellite Applications Facility (CMSAF), and Pathfinder Atmospheres-Extended (PATMOS-x) datasets, were found to be correlated with DARDAR over a large IWP range (~20–7000 g m -2 ). The random errors of the collocated data sets have a close to lognormal distribution, and the combined random error of MODIS and DARDAR is less than a factor of 2, which also sets the upper limit for MODIS alone. In the same way, the upper limit for the random error of all considered data sets is determined. Data sets based on passive microwave measurements, microwave surface and precipitation products system (MSPPS), microwave integrated retrieval system (MiRS), and collocated microwave only (CMO), are largely correlated with DARDAR for IWP values larger than approximately 700 g m -2 . The combined uncertainty between these data sets and DARDAR in this range is slightly less MODIS-DARDAR, but the systematic bias is nearly an order of magnitude.

Climatology, storm morphologies, and environments of tornadoes in the British Isles: 1980–2012

A climatology is developed for tornadoes during 1980–2012 in the British Isles, defined in this article as England, Scotland, Wales, Northern Ireland, Republic of Ireland, Channel Islands, and the Isle of Man. The climatology includes parent storm type, interannual variability, annual and diurnal cycles, intensities, oc- currence of outbreaks (defined as three or more tornadoes in the same day), geographic distribution, and environmental conditions derived from proximity soundings of tornadoes. Tornado reports are from the Tornado and Storm Research Organization (TORRO). Over the 33 years, there were a mean of 34.3 tor- nadoes and 19.5 tornado days (number of days in which at least one tornado occurred) annually. Tornadoes and tornado outbreaks were most commonly produced from linear storms, defined as radar signatures at least 75 km long and approximately 3 times as long as wide. Most (78%) tornadoes occurred in England. The probability of a tornado within 10 km of a point was highest in the south, southeast, and west of England. On average, there were 2.5 tornado outbreaks every year. Where intensity was known, 95% of tornadoes were classified as F0 or F1 with the remainder classified as F2. There were no tornadoes rated F3 or greater during this time period. Tornadoes occurred throughout the year with a maximum from May through October. Finally, tornadoes tended to occur in low-CAPE, high-shear environments. Tornadoes in the British Isles were difficult to predict using only sounding-derived parameters because there were no clear thresholds between null, tornadic, outbreak, and significant tornado cases.

Observations of supercooling and frazil ice formation in the Laptev Sea coastal polynya

This paper examines a hydrographic response to the wind‐driven coastal polynya activity over the southeastern Laptev Sea shelf for April–May 2008, using a combination of Environmental Satellite (Envisat) advanced synthetic aperture radar (ASAR) and TerraSAR‐X satellite imagery, aerial photography, meteorological data, and SBE‐37 salinity‐temperature‐depth and acoustic Doppler current profiler land‐fast ice edgemoored instruments. When ASAR observed the strongest end‐of‐April polynya event with frazil ice formation, the moored instruments showed maximal acoustical scattering within the surface mixed layer, and the seawater temperatures were either at or 0.02°C below freezing. We also find evidence of the persistent horizontal temperature and salinity gradients across the fast ice edge to have the signature of geostrophic flow adjustment as predicted by polynya models.

The COnvective Precipitation Experiment (COPE): investigating the origins of heavy precipitation in the southwestern UK

A recent field campaign in southwest England used numerical modeling integrated with aircraft and radar observations to investigate the dynamic and microphysical interactions that can result in heavy convective precipitation. The COnvective Precipitation Experiment (COPE) was a joint UK-US field campaign held during the summer of 2013 in the southwest peninsula of England, designed to study convective clouds that produce heavy rain leading to flash floods. The clouds form along convergence lines that develop regularly due to the topography. Major flash floods have occurred in the past, most famously at Boscastle in 2004. It has been suggested that much of the rain was produced by warm rain processes, similar to some flash floods that have occurred in the US. The overarching goal of COPE is to improve quantitative convective precipitation forecasting by understanding the interactions of the cloud microphysics and dynamics and thereby to improve NWP model skill for forecasts of flash floods. Two research aircraft, the University of Wyoming King Air and the UK BAe 146, obtained detailed in situ and remote sensing measurements in, around, and below storms on several days. A new fast-scanning X-band dual-polarization Doppler radar made 360-deg volume scans over 10 elevation angles approximately every 5 minutes, and was augmented by two UK Met Office C-band radars and the Chilbolton S-band radar. Detailed aerosol measurements were made on the aircraft and on the ground. This paper: (i) provides an overview of the COPE field campaign and the resulting dataset; (ii) presents examples of heavy convective rainfall in clouds containing ice and also in relatively shallow clouds through the warm rain process alone; and (iii) explains how COPE data will be used to improve high-resolution NWP models for operational use.

The EarthCARE satellite: the next step forward in global measurements of clouds, aerosols, precipitation, and radiation

The 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 cloud profiling radar, with 7 dB more sensitivity than CloudSat, will detect more thin clouds and its Doppler capability will provide novel information on convection, precipitating ice particle, and raindrop fall speeds. EarthCARE’s 355-nm high-spectral-resolution lidar will measure directly and accurately cloud and aerosol extinction and optical depth. Combining this with backscatter and polarization information should lead to an unprecedented ability to identify aerosol type. The multispectral imager will provide a context for, and the ability to construct, the cloud and aerosol distribution in 3D domains around the narrow 2D retrieved cross section. The consistency of the retrievals will be assessed to within a target of ±10 W m–2 on the (10 km)2 scale by comparing the multiview broadband radiometer observations to the top-of-atmosphere fluxes estimated by 3D radiative transfer models acting on retrieved 3D domains.

The role of evaporating showers in the transfer of sting-jet momentum to the surface

The most damaging winds in a severe extratropical cyclone often occur just ahead of the evaporating ends of cloud filaments emanating from the so-called cloud head. These winds are associated with low-level jets (LLJs), sometimes occurring just above the boundary layer. The question then arises as to how the high momentum is transferred to the surface. An opportunity to address this question arose when the severe ‘St Jude's Day’ windstorm travelled across southern England on 28 October 2013. We have carried out a mesoanalysis of a network of 1 min resolution automatic weather stations and high-resolution Doppler radar scans from the sensitive S-band Chilbolton Advanced Meteorological Radar (CAMRa), along with satellite and radar network imagery and numerical weather prediction products. We show that, although the damaging winds occurred in a relatively dry region of the cyclone, there was evidence within the LLJ of abundant precipitation residues from shallow convective clouds that were evaporating in a localized region of descent. We find that pockets of high momentum were transported towards the surface by the few remaining actively precipitating convective clouds within the LLJ and also by precipitation-free convection in the boundary layer that was able to entrain evaporatively cooled air from the LLJ. The boundary-layer convection was organized in along-wind rolls separated by 500 to about 3000 m, the spacing varying according to the vertical extent of the convection. The spacing was greatest where the strongest winds penetrated to the surface. A run with a medium-resolution version of the Weather Research and Forecasting (WRF) model was able to reproduce the properties of the observed LLJ. It confirmed the LLJ to be a sting jet, which descended over the leading edge of a weaker cold-conveyor-belt jet.

A new method for the characterisation and verification of local spatial predictability for convective scale ensembles

The use of kilometre-scale ensembles in operational forecasting provides new challenges for forecast interpretation and evaluation to account for uncertainty on the convective scale. A new neighbourhood based method is presented for evaluating and characterising the local predictability variations from convective scale ensembles. Spatial scales over which ensemble forecasts agree (agreement scales, S^A) are calculated at each grid point ij, providing a map of the spatial agreement between forecasts. By comparing the average agreement scale obtained from ensemble member pairs (S^A(mm)_ij), with that between members and radar observations (S^A(mo)_ij), this approach allows the location-dependent spatial spread-skill relationship of the ensemble to be assessed. The properties of the agreement scales are demonstrated using an idealised experiment. To demonstrate the methods in an operational context the S^A(mm)_ij and S^A(mo)_ij are calculated for six convective cases run with the Met Office UK Ensemble Prediction System. The S^A(mm)_ij highlight predictability differences between cases, which can be linked to physical processes. Maps of S^A(mm)_ij are found to summarise the spatial predictability in a compact and physically meaningful manner that is useful for forecasting and for model interpretation. Comparison of S^A(mm)_ij and S^A(mo)_ij demonstrates the case-by-case and temporal variability of the spatial spread-skill, which can again be linked to physical processes.

High-precision measurements of the co-polar correlation coefficient: non-Gaussian errors and retrieval of the dispersion parameter µ in rainfall

The co-polar correlation coefficient (ρhv) has many applications, including hydrometeor classification, ground clutter and melting layer identification, interpretation of ice microphysics and the retrieval of rain drop size distributions (DSDs). However, we currently lack the quantitative error estimates that are necessary if these applications are to be fully exploited. Previous error estimates of ρhv rely on knowledge of the unknown "true" ρhv and implicitly assume a Gaussian probability distribution function of ρhv samples. We show that frequency distributions of ρhv estimates are in fact highly negatively skewed. A new variable: L = -log10(1 - ρhv) is defined, which does have Gaussian error statistics, and a standard deviation depending only on the number of independent radar pulses. This is verified using observations of spherical drizzle drops, allowing, for the first time, the construction of rigorous confidence intervals in estimates of ρhv. In addition, we demonstrate how the imperfect co-location of the horizontal and vertical polarisation sample volumes may be accounted for. The possibility of using L to estimate the dispersion parameter (µ) in the gamma drop size distribution is investigated. We find that including drop oscillations is essential for this application, otherwise there could be biases in retrieved µ of up to ~8. Preliminary results in rainfall are presented. In a convective rain case study, our estimates show µ to be substantially larger than 0 (an exponential DSD). In this particular rain event, rain rate would be overestimated by up to 50% if a simple exponential DSD is assumed.