Bayesian retrieval of complete posterior PDFs of oceanic rain rate from microwave observations

A new Bayesian algorithm for retrieving surface rain rate from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) over the ocean is presented, along with validations against estimates from the TRMM Precipitation Radar (PR). The Bayesian approach offers a rigorous basis for optimally combining multichannel observations with prior knowledge. While other rain-rate algorithms have been published that are based at least partly on Bayesian reasoning, this is believed to be the first self-contained algorithm that fully exploits Bayes’s theorem to yield not just a single rain rate, but rather a continuous posterior probability distribution of rain rate. To advance the understanding of theoretical benefits of the Bayesian approach, sensitivity analyses have been conducted based on two synthetic datasets for which the “true” conditional and prior distribution are known. Results demonstrate that even when the prior and conditional likelihoods are specified perfectly, biased retrievals may occur at high rain rates. This bias is not the result of a defect of the Bayesian formalism, but rather represents the expected outcome when the physical constraint imposed by the radiometric observations is weak owing to saturation effects. It is also suggested that both the choice of the estimators and the prior information are crucial to the retrieval. In addition, the performance of the Bayesian algorithm herein is found to be comparable to that of other benchmark algorithms in real-world applications, while having the additional advantage of providing a complete continuous posterior probability distribution of surface rain rate.

The TAMORA algorithm: satellite rainfall estimates over West Africa using multi-spectral SEVIRI data

A multi-spectral rainfall estimation algorithm has been developed for the Sahel region of West Africa with the purpose of producing accumulated rainfall estimates for drought monitoring and food security. Radar data were used to calibrate multi-channel SEVIRI data from MSG, and a probability of rainfall at several different rain-rates was established for each combination of SEVIRI radiances. Radar calibrations from both Europe (the SatPrecip algorithm) and Niger (TAMORA algorithm) were used. 10 day estimates were accumulated from SatPrecip and TAMORA and compared with kriged gauge data and TAMSAT satellite rainfall estimates over West Africa. SatPrecip was found to produce large overestimates for the region, probably because of its non-local calibration. TAMORA was negatively biased for areas of West Africa with relatively high rainfall, but its skill was comparable to TAMSAT for the low-rainfall region climatologically similar to its calibration area around Niamey. These results confirm the high importance of local calibration for satellite-derived rainfall estimates. As TAMORA shows no improvement in skill over TAMSAT for dekadal estimates, the extra cloud-microphysical information provided by multi-spectral data may not be useful in determining rainfall accumulations at a ten day timescale. Work is ongoing to determine whether it shows improved accuracy at shorter timescales.

On differentiating ground clutter and insect echoes from Doppler weather radars using archived data

Normally wind measurements from Doppler radars rely on the presence of rain. During fine weather, insects become a potential radar target for wind measurement. However, it is difficult to separate ground clutter and insect echoes when spectral or polarimetric methods are not available. Archived reflectivity and velocity data from repeated scans provide alternative methods. The probability of detection (POD) method, which maps areas with a persistent signal as ground clutter, is ineffective when most scans also contain persistent insect echoes. We developed a clutter detection method which maps the standard deviation of velocity (SDV) over a large number of scans, and can differentiate insects and ground clutter close to the radar. Beyond the range of persistent insect echoes, the POD method more thoroughly removes ground clutter. A new, pseudo-probability clutter map was created by combining the POD and SDV maps. The new map optimised ground clutter detection without removing insect echoes.

Synthesis of broadband anti-reflection coatings for spaceflight infrared optics

A synthesis method is outlined for the design of broadband anti-reflection coatings for use in spaceborne infrared optics. The Golden Section optimisation routine is used to make a search, using designated non-absorptive dielectric thin film combinations, for the coating design which fulfils the required spectral requirements using the least number of layers and different materials. Three examples are given of coatings designed by this method : (I) 1µm to 12µm anti-reflection coating on Zinc Sulphide using Zinc Sulphide and Yttrium Fluoride thin film materials. (ii) 2µm to 14µm anti-reflection coating on Germanium using Germanium and Ytterbium Fluoride thin film materials. (iii) 6µm to 17µm anti-reflection coating on Germanium using Lead Telluride, Zinc Selenide and Barium Fluoride. The measured spectral performance of the manufactured 6µm to 17µm coating on Germanium is given. This is the anti-reflection coating for the germanium optics in the NASA Cassini Orbiter CIRS instrument.

Advancements in the estimation of ice particle fall speeds using laboratory and field measurements

Accurate estimates for the fall speed of natural hydrometeors are vital if their evolution in clouds is to be understood quantitatively. In this study, laboratory measurements of the terminal velocity vt for a variety of ice particle models settling in viscous fluids, along with wind-tunnel and field measurements of ice particles settling in air, have been analyzed and compared to common methods of computing vt from the literature. It is observed that while these methods work well for a number of particle types, they fail for particles with open geometries, specifically those particles for which the area ratio Ar is small (Ar is defined as the area of the particle projected normal to the flow divided by the area of a circumscribing disc). In particular, the fall speeds of stellar and dendritic crystals, needles, open bullet rosettes, and low-density aggregates are all overestimated. These particle types are important in many cloud types: aggregates in particular often dominate snow precipitation at the ground and vertically pointing Doppler radar measurements. Based on the laboratory data, a simple modification to previous computational methods is proposed, based on the area ratio. This new method collapses the available drag data onto an approximately universal curve, and the resulting errors in the computed fall speeds relative to the tank data are less than 25% in all cases. Comparison with the (much more scattered) measurements of ice particles falling in air show strong support for this new method, with the area ratio bias apparently eliminated.

Exploring the land–ocean contrast in convective vigor using islands

Moist convection is well known to be generally more intense over continental than maritime regions, with larger updraft velocities, graupel, and lightning production. This study explores the transition from maritime to continental convection by comparing the trends in Tropical Rainfall Measuring Mission (TRMM) radar and microwave (37 and 85 GHz) observations over islands of increasing size to those simulated by a cloud-resolving model. The observed storms were essentially maritime over islands of <100 km2 and continental over islands >10 000 km2, with a gradual transition in between. Equivalent radar and microwave quantities were simulated from cloud-resolving runs of the Weather Research and Forecasting model via offline radiation codes. The model configuration was idealized, with islands represented by regions of uniform surface heat flux without orography, using a range of initial sounding conditions without strong horizontal winds or aerosols. Simulated storm strength varied with initial sounding, as expected, but also increased sharply with island size in a manner similar to observations. Stronger simulated storms were associated with higher concentrations of large hydrometeors. Although biases varied with different ice microphysical schemes, the trend was similar for all three schemes tested and was also seen in 2D and 3D model configurations. The successful reproduction of the trend with such idealized forcing supports previous suggestions that mesoscale variation in surface heating—rather than any difference in humidity, aerosol, or other aspects of the atmospheric state—is the main reason that convection is more intense over continents and large islands than over oceans. Some dynamical storm aspects, notably the peak rainfall and minimum surface pressure low, were more sensitive to surface forcing than to the atmospheric sounding or ice scheme. Large hydrometeor concentrations and simulated microwave and radar signatures, however, were at least as sensitive to initial humidity levels as to surface forcing and were more sensitive to the ice scheme. Issues with running the TRMM simulator on 2D simulations are discussed, but they appear to be less serious than sensitivities to model microphysics, which were similar in 2D and 3D. This supports the further use of 2D simulations to economically explore modeling uncertainties.

Observations of ice multiplication in a weakly convective cell embedded in supercooled mid-level stratus

Simultaneous observations of cloud microphysical properties were obtained by in-situ aircraft measurements and ground based Radar/Lidar. Widespread mid-level stratus cloud was present below a temperature inversion (~5 °C magnitude) at 3.6 km altitude. Localised convection (peak updraft 1.5 m s−1) was observed 20 km west of the Radar station. This was associated with convergence at 2.5 km altitude. The convection was unable to penetrate the inversion capping the mid-level stratus. The mid-level stratus cloud was vertically thin (~400 m), horizontally extensive (covering 100 s of km) and persisted for more than 24 h. The cloud consisted of supercooled water droplets and small concentrations of large (~1 mm) stellar/plate like ice which slowly precipitated out. This ice was nucleated at temperatures greater than −12.2 °C and less than −10.0 °C, (cloud top and cloud base temperatures, respectively). No ice seeding from above the cloud layer was observed. This ice was formed by primary nucleation, either through the entrainment of efficient ice nuclei from above/below cloud, or by the slow stochastic activation of immersion freezing ice nuclei contained within the supercooled drops. Above cloud top significant concentrations of sub-micron aerosol were observed and consisted of a mixture of sulphate and carbonaceous material, a potential source of ice nuclei. Particle number concentrations (in the size range 0.1

Evaluation of two "integrated" polarimetric Quantitative Precipitation Estimation (QPE) algorithms at C-band

Two so-called “integrated” polarimetric rate estimation techniques, ZPHI (Testud et al., 2000) and ZZDR (Illingworth and Thompson, 2005), are evaluated using 12 episodes of the year 2005 observed by the French C-band operational Trappes radar, located near Paris. The term “integrated” means that the concentration parameter of the drop size distribution is assumed to be constant over some area and the algorithms retrieve it using the polarimetric variables in that area. The evaluation is carried out in ideal conditions (no partial beam blocking, no ground-clutter contamination, no bright band contamination, a posteriori calibration of the radar variables ZH and ZDR) using hourly rain gauges located at distances less than 60 km from the radar. Also included in the comparison, for the sake of benchmarking, is a conventional Z = 282R1.66 estimator, with and without attenuation correction and with and without adjustment by rain gauges as currently done operationally at Météo France. Under those ideal conditions, the two polarimetric algorithms, which rely solely on radar data, appear to perform as well if not better, pending on the measurements conditions (attenuation, rain rates, …), than the conventional algorithms, even when the latter take into account rain gauges through the adjustment scheme. ZZDR with attenuation correction is the best estimator for hourly rain gauge accumulations lower than 5 mm h−1 and ZPHI is the best one above that threshold. A perturbation analysis has been conducted to assess the sensitivity of the various estimators with respect to biases on ZH and ZDR, taking into account the typical accuracy and stability that can be reasonably achieved with modern operational radars these days (1 dB on ZH and 0.2 dB on ZDR). A +1 dB positive bias on ZH (radar too hot) results in a +14% overestimation of the rain rate with the conventional estimator used in this study (Z = 282R^1.66), a -19% underestimation with ZPHI and a +23% overestimation with ZZDR. Additionally, a +0.2 dB positive bias on ZDR results in a typical rain rate under- estimation of 15% by ZZDR.

Evidence that ice forms primarily in supercooled liquid clouds at temperatures > -27C

Using 4 years of radar and lidar observations of layer clouds from the Chilbolton Observatory in the UK, we show that almost all (95%) ice particles formed at temperatures >-20°C appear to originate from supercooled liquid clouds. At colder temperatures, there is a monotonic decline in the fraction of liquid-topped ice clouds: 50% at -27°C, falling to zero at -37°C (where homogeneous freezing of water droplets occurs). This strongly suggests that deposition nucleation plays a relatively minor role in the initiation of ice in mid-level clouds. It also means that the initial growth of the ice particles occurs predominantly within a liquid cloud, a situation which promotes rapid production of precipitation via the Bergeron-Findeison mechanism.

On the importance of interplanetary magnetic field (By)on polar cap patch formation

A number of poleward moving events were observed between 1130 and 1300 UT on 11 February 2004, during periods of southward interplanetary magnetic field (IMF), while the steerable antenna of the European Incoherent Scatter (EISCAT) Svalbard radar (ESR)and the Tromsø VHF radar pointed nearly northward at low elevation. In this interval, simultaneous SuperDARN CUTLASS Finland radar measurements showed poleward moving radar aurora forms (PMRAFs) which appeared very similar to the density enhancements observed by the ESR northward pointing antenna. These events appeared quasiperiodically with a period of about 10 min. Comparing the observations from the above three radars, it is inferred that there is an almost one‐to‐one correspondence between the poleward moving plasma concentration enhancements (PMPCEs) observed by the ESR and the VHF radar and the PMRAFs measured by the CUTLASS Finland radar. These observations are consistent with the interpretation that the polar cap patch material was generated by photoionization at subauroral latitudes and that the plasma was structured by bursts of magnetopause reconnection giving access to the polar cap. There is clear evidence that plasma structuring into patches was dependent on the variability in IMF |By|. The duration of these events implies that the average evolution time of the newly opened flux tubes from the subauroral region to the polar cap was about 33 min.

The plastic self organising map

A novel extension to Kohonen's self-organising map, called the plastic self organising map (PSOM), is presented. PSOM is unlike any other network because it only has one phase of operation. The PSOM does not go through a training cycle before testing, like the SOM does and its variants. Each pattern is thus treated identically for all time. The algorithm uses a graph structure to represent data and can add or remove neurons to learn dynamic nonstationary pattern sets. The network is tested on a real world radar application and an artificial nonstationary problem.

A comparison among four different retrieval methods for ice-cloud properties using data from CloudSat, CALIPSO, and MODIS

The A-Train constellation of satellites provides a new capability to measure vertical cloud profiles that leads to more detailed information on ice-cloud microphysical properties than has been possible up to now. A variational radar–lidar ice-cloud retrieval algorithm (VarCloud) takes advantage of the complementary nature of the CloudSat radar and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar to provide a seamless retrieval of ice water content, effective radius, and extinction coefficient from the thinnest cirrus (seen only by the lidar) to the thickest ice cloud (penetrated only by the radar). In this paper, several versions of the VarCloud retrieval are compared with the CloudSat standard ice-only retrieval of ice water content, two empirical formulas that derive ice water content from radar reflectivity and temperature, and retrievals of vertically integrated properties from the Moderate Resolution Imaging Spectroradiometer (MODIS) radiometer. The retrieved variables typically agree to within a factor of 2, on average, and most of the differences can be explained by the different microphysical assumptions. For example, the ice water content comparison illustrates the sensitivity of the retrievals to assumed ice particle shape. If ice particles are modeled as oblate spheroids rather than spheres for radar scattering then the retrieved ice water content is reduced by on average 50% in clouds with a reflectivity factor larger than 0 dBZ. VarCloud retrieves optical depths that are on average a factor-of-2 lower than those from MODIS, which can be explained by the different assumptions on particle mass and area; if VarCloud mimics the MODIS assumptions then better agreement is found in effective radius and optical depth is overestimated. MODIS predicts the mean vertically integrated ice water content to be around a factor-of-3 lower than that from VarCloud for the same retrievals, however, because the MODIS algorithm assumes that its retrieved effective radius (which is mostly representative of cloud top) is constant throughout the depth of the cloud. These comparisons highlight the need to refine microphysical assumptions in all retrieval algorithms and also for future studies to compare not only the mean values but also the full probability density function.

Advances in continuously profiling the thermodynamic state of the boundary layer: integration of measurements and methods

This paper describes advances in ground-based thermodynamic profiling of the lower troposphere through sensor synergy. The well-documented integrated profiling technique (IPT), which uses a microwave profiler, a cloud radar, and a ceilometer to simultaneously retrieve vertical profiles of temperature, humidity, and liquid water content (LWC) of nonprecipitating clouds, is further developed toward an enhanced performance in the boundary layer and lower troposphere. For a more accurate temperature profile, this is accomplished by including an elevation scanning measurement modus of the microwave profiler. Height-dependent RMS accuracies of temperature (humidity) ranging from 0.3 to 0.9 K (0.5–0.8 g m−3) in the boundary layer are derived from retrieval simulations and confirmed experimentally with measurements at distinct heights taken during the 2005 International Lindenberg Campaign for Assessment of Humidity and Cloud Profiling Systems and its Impact on High-Resolution Modeling (LAUNCH) of the German Weather Service. Temperature inversions, especially of the lower boundary layer, are captured in a very satisfactory way by using the elevation scanning mode. To improve the quality of liquid water content measurements in clouds the authors incorporate a sophisticated target classification scheme developed within the European cloud observing network CloudNet. It allows the detailed discrimination between different types of backscatterers detected by cloud radar and ceilometer. Finally, to allow IPT application also to drizzling cases, an LWC profiling method is integrated. This technique classifies the detected hydrometeors into three different size classes using certain thresholds determined by radar reflectivity and/or ceilometer extinction profiles. By inclusion into IPT, the retrieved profiles are made consistent with the measurements of the microwave profiler and an LWC a priori profile. Results of IPT application to 13 days of the LAUNCH campaign are analyzed, and the importance of integrated profiling for model evaluation is underlined.

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 NASA's 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 profiles requires a combination of active and passive instruments, and this will be achieved by combining the radar data of CloudSat with data from other active and passive sensors of the constellation. This paper describes the underpinning science and general overview of the mission, provides some idea of the expected products and anticipated application of these products, and the potential capability of the A-Train for cloud observations. Notably, the CloudSat mission is expected to stimulate new areas of research on clouds. The mission also provides an important opportunity to demonstrate active sensor technology for future scientific and tactical applications. The CloudSat mission is a partnership between NASA's JPL, the Canadian Space Agency, Colorado State University, the U.S. Air Force, and the U.S. Department of Energy.

The vertical cloud structure of the West African monsoon: a 4 year climatology using CloudSat and CALIPSO

The West African summer monsoon (WAM) is an important driver of the global climate and locally provides most of the annual rainfall. A solid climatological knowledge of the complex vertical cloud structure is invaluable to forecasters and modelers to improve the understanding of the WAM. In this paper, 4 years of data from the CloudSat profiling radar and CALIPSO are used to create a composite zonal mean vertical cloud and precipitation structure for the WAM. For the first time, the near-coincident vertical radar and lidar profiles allow for the identification of individual cloud types from optically thin cirrus and shallow cumulus to congestus and deep convection. A clear diurnal signal in zonal mean cloud structure is observed for the WAM, with deep convective activity enhanced at night producing extensive anvil and cirrus, while daytime observations show more shallow cloud and congestus. A layer of altocumulus is frequently observed over the Sahara at night and day, extending southward to the coastline, and the majority of this cloud is shown to contain supercooled liquid in the top. The occurrence of deep convective systems and congestus in relation to the position of the African easterly jet is studied, but only the daytime cumulonimbus distribution indicates some influence of the jet position.