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In this study, the grounded‐based Ka‐band millimetre‐wave cloud radar (MMCR) is used to analyse the cloud characteristics over the western Tianshan mountains. The cloud top height (CTH) obtained by MMCR is verified by comparing it with the Fengyun‐4A (FY‐4A) observations. Overall, the MMCR‐obtained CTHs are attenuated and lower than the FY‐4A‐obtained CTHs under precipitation conditions. Thus, the FY‐4A data is used to complement MMCR data when there is rainfall. The diurnal, seasonal variation and vertical structures of the cloud are further examined using the combination of MMCR and FY‐4A. The result indicates that the CTH and cloud base height (CBH) are highest in summer and lowest in winter. Also, clouds tend to form frequently at night in spring, summer, and winter. Although in autumn, clouds tend to form most frequently in the morning. This may be related to the diurnal variation of temperature, humidity, wind speed, and wind direction. Moreover, the CTHs occur most frequently at heights of 8–9 km in spring and autumn, 9–10 km in summer, and 7–8 km in winter. The high CTHs caused by the strong convective activities in summer may be related to sufficient water vapour transport. These corrected CTH data are also used to classify cloud types, the results indicate that the proportion of high clouds is highest in summer, while the proportion of medium clouds is lower than in the other three seasons. Also, the average CTH of the low, medium, and high clouds is highest in summer and lowest in winter. [ABSTRACT FROM AUTHOR]

The understanding of large‐scale rainfall microphysical characteristics plays a significant role in meteorology, hydrology and natural hazards managements. Traditional instruments for estimating raindrop size distribution (DSD), including disdrometers and ground dual‐polarization radars, are available only in limited areas. However, the development of space‐based radars and mesoscale numerical weather prediction models would allow for DSD estimation on a large scale. This study investigated the performance of the weather research and forecasting (WRF) model and the global precipitation measurement mission (GPM) dual‐frequency precipitation radar for DSD retrieval under different conditions. The DSD parameters (Dm and Nw), rain rate (R), rainfall kinetic energy (KE) and radar reflectivity (Z) were estimated in Chilbolton, United Kingdom, by using long‐term disdrometer observations for validation. The rainfall kinetic energy–rain rate (KE–R) and radar reflectivity–rain rate (Z–R) relationships were explored using a disdrometer, the WRF model and GPM. It was found that the DSD parameter distribution trends of the three approaches are similar although the WRF model has larger Dm and smaller Nw values. In terms of the rainfall microphysical relationship, GPM performs better when both Ku‐ and Ka‐band precipitation radars (KuPR and KaPR) observe precipitation simultaneously (R > 0.5 mm h−1), while the WRF model shows high accuracy in light rain (R < 0.5 mm h−1). The fusion of GPM and WRF model is recommended for the improved understanding of rainfall microphysical characteristics in ungauged areas. [ABSTRACT FROM AUTHOR]

A radar reflectivity observation operator with ice-phase hydrometeors was employed for the assimilation of the real radar observations of a squall line on June 14, 2009, over Anhui and Jiangsu provinces in China. The threedimensional variational (3DVAR) data-assimilation system along with the Weather Research and Forecasting (WRF) model was applied. The impact of assimilating radar radial velocity and reflectivity is investigated by analysing the meteorological conditions for different stages of the squall line. The results show that the radar data-assimilation experiments adjust and improve the initial fields to some extent for the squall line with the cycling data-assimilation strategy. The analysed and predicted radar composite reflectivity is significantly improved with the use of the ice-phase hydrometeors observation operator. The improvements are illustrated in terms of the wind fields for both the surface and upper level, the radar reflectivity, the surface perturbation potential temperature and the surface perturbation pressure. In addition, the forecast skills of the precipitation are improved noticeably for both the location and the intensity after using the ice-phase hydrometeors radar observation operator. [ABSTRACT FROM AUTHOR]

Dual‐frequency radar (DFR) could improve the accuracy of estimating the microphysical properties of rain profiles. Unfortunately, factors that cause inaccuracies in retrieved rain profiles include uncertainties in raindrop size distribution (DSD) parameterizations, DFR retrieval methods and radar measurement errors. The primary objective of this study was to assess the uncertainties in retrieving rain profiles to offer insight into application considerations for ground‐based DFRs in China. The uncertainties caused by DSD models are assessed by comparing attenuation coefficients and rain rates from DFR algorithms with those directly derived from DSD spectra from disdrometers. Then, based on a DSD model, the impacts of retrieval methods, radar range resolution, measurement error and errors in temperature on DFR retrievals are explored by comparing the rain profiles obtained using DFR algorithms with those directly output from the Weather Research and Forecasting model. Overall, the impact of measurement error on the DFR retrievals is relatively significant for both the forward retrieval method and the iterative backward retrieval method and should be eliminated in practical applications. At an individual uncertainty level, the impact of DSD parameterizations on the retrievals from the dual‐frequency technique is less than ±10% bias when the shape factor μ of the gamma distribution ranges from 2 to 4. The retrieved rain profiles from the forward retrieval method are relatively sensitive to radar range resolution and the retrieval performance can be improved with increasing the range resolution, while the iterative backward retrieval method exhibits stable retrieval performance. The impact of temperature errors on the retrieved rain profiles is negligible. [ABSTRACT FROM AUTHOR]

A millimetre‐wave cloud radar has limitations for observing heavy rainfall because the short wavelength leads to strong attenuation from raindrops. However, recent studies have attempted to estimate the rain rate using attenuation, which becomes greater as the rain intensity increases. The rain‐rate‐retrieval algorithm is developed in the present paper using the Ka‐band cloud radar (KaCR) installed at the Boseong Global Standard Observatory (BGSO) in the Republic of Korea. First, rain profiles were identified using the threshold of reflectivity (Z) and Doppler velocity (DV) averaged for the analysis of layer that minimized the effects of the receiver saturation and classified as low or high rain‐rate cases using the averaged DV. The reflectivity and rain‐rate (Z‐R) relationship was then derived for low rain‐rate cases that showed insignificant effects for signal attenuation. On the other hand, the attenuation and rain‐rate (A‐R) relationship was derived using high rain‐rate cases that showed the dominant effects of attenuation. The attenuation was calculated using a reflectivity gradient between the upper and lower boundaries of the attenuated layer. Finally, the rain‐rate‐retrieval algorithm was designed using the derived Z‐R and A‐R relationship and then applied back to the KaCR. The estimated rain rate in the KaCR was a similar trend to the observed rain rate in the optical rain gauge (ORG), but slightly underestimated. [ABSTRACT FROM AUTHOR]

The background error covariance is an important component in data assimilation systems, which largely dominates the error correlations between different analysis variables. This study aimed to assess the impact of the determination of the background error length and variance scale factors on the track and intensity forecast of hurricane systems for radar radial velocity (Vr) data assimilation. To test the sensitivity of the background error length scale and variance scale, multiple data assimilation analyses were performed using a 3D variational data assimilation method with a parameter‐sweeping strategy for the case of Hurricane Ike (2008) by varying the decorrelation length scale (LEN_SCALING) and the variance (VAR_SCALING) of the background error step by step. It is shown that, in radar Vr data assimilation, generally smaller variance scale and length scale factors tend to improve the prediction of the hurricane track. Setting both VAR_SCALING and LEN_SCALING equivalent to 0.4 provides a better track than other combinations. In particular, the impact of the variance scale factor on the track forecasts is larger than that of the length scale factor. There is no direct impact of tuning variance scales and length scales on the forecast intensity for the first few forecast hours. Tuning length scale factors less than 0.4 provides smaller forecast errors of minimum sea level pressure and maximum surface wind with increasing forecast lead time. [ABSTRACT FROM AUTHOR]

The paper is focused on the problem of weather radar precipitation compositing and aims to produce the data for the whole measurement domain in a way that ensures the quality of the composite is as high as possible. This is achieved by employing information about the data quality in the form of a quality index (QI) map. Different techniques that take the QI values as the compositing criterion are investigated; they apply the maximum or the weighted mean from particular radars. The proposed method employs a combination of the QI value and the distance from the radar site as a criterion in a weighted mean approach. Different versions of the quality‐based techniques are compared with standard techniques that are based on the maximum of estimated precipitation and distance from the nearest radar site. Verification of the techniques was performed employing statistical metrics by comparison against rain gauge observations. Moreover qualitative verification with assessment of seamlessness and the presence of contamination was carried out on long‐term accumulations of the composites generated by means of different methods. The proposed method based on QI value and distance from the radar site was found to be the most suitable for radar precipitation compositing. [ABSTRACT FROM AUTHOR]

A spatially and temporally varying Bayesian inflation algorithm was applied to the assimilation of the real radar data of a mesoscale convective system (MCS), which occurred on May 8–9, 2007 in Oklahoma and Texas (USA), using the ensemble square root filter (EnSRF) within the advanced regional prediction system (ARPS). For comparison purposes, two radar data assimilation and forecast experiments using Bayesian inflation and regular multiplicative inflation algorithms were performed. Results indicated that the analysis reflectivity structure of the MCS using the Bayesian inflation method appeared better than when using the multiplicative inflation method. The Bayesian inflation method also reduced the root mean square innovations (RMSIs) and increased the ensemble spreads for reflectivity and radial velocity. Furthermore, the reduction of RMSIs was related to the higher Bayesian inflation parameter assigned to the background. The flow‐dependent characteristic of the Bayesian inflation parameter means it varies with space and model state variables. This successful analysis helped improve subsequent forecast reflectivity and precipitation, especially for stratiform precipitation. It also promoted the development of the MCS with a strong surface cold pool and outflow in the convective regions. Quantitative reflectivity and precipitation forecast skills were also improved in the Bayesian inflation experiment. These encouraging results demonstrated the greater potential of using the Bayesian inflation algorithm for EnSRF radar data assimilation compared with multiplicative inflation. [ABSTRACT FROM AUTHOR]

ABSTRACT The ensemble transform Kalman filter based ensemble 3D variational ( ETKF-En3DVAR) data assimilation ( DA) system is employed to evaluate the potential value of assimilating radar radial wind ( Vr) data for the analysis and forecasting of Typhoon Saomai (2006). The DA system conducted cycling assimilation every 30 min when Saomai started to enter radar coverage. Within the DA cycles, the control analysis was updated by the ETKF-En3DVAR algorithm whereas the forecast ensemble perturbations in the hybrid scheme were updated by the ETKF algorithm. The benefits from the use of the flow-dependent ensemble covariance are explored by comparing the analysis increments, analysis and subsequent forecasts from the hybrid scheme with those from a pure 3DVAR using static background error covariance. Sensitivity to the horizontal correlation scale in the 3DVAR and the vertical covariance localization in the hybrid are also explored. The reduced horizontal correlation scale in the 3DVAR yields much more reasonable circulation analyses than the default scale. The vertical covariance localization scale specified in terms of geometric height instead of model levels allows for desirable spreading of Vr data to the surface. It seems that the assimilation with the hybrid method leads to further improved vortex intensity forecast and track forecast of the typhoon compared to those in the analyses from the global forecast system and 3DVAR. The results also indicated that the hybrid has a significant effect on the 12 h accumulated rainfall forecasts. Such improvements for analysis and forecast are probably due to the use of the flow-dependent background error covariance. [ABSTRACT FROM AUTHOR]

ABSTRACT The goal of the present study was to exploit the volumetric data from the Wideumont weather radar to estimate the occurrence and severity of hail over a period of 10 years (2003-2012). The radar is located in the southeastern part of Belgium and its domain covers Belgium, Luxembourg and some parts of Germany, France and The Netherlands. Two hail detection algorithms were used for detecting hail falls in the volumetric radar data. The algorithms provide an empirical estimation of the probability of hail ( POH) and the probability of severe hail ( POSH). The study shows that post-processing of probabilities by means of the advection correction significantly influences the statistical results about hail occurrence. The advection correction is very effective in reducing the 'fishbone effect' due to a temporal sampling of the radar data that is too low, which has an impact on the geographical distribution of the hail fall frequencies over the study domain. The post-processed POH and POSH datasets are verified against hail reports at the ground. The statistics obtained show that the diurnal cycle of hail falls has a pronounced peak in the 1500-1600 UTC (local solar time + 1 h) time interval with 28% of all hail events occurring in July and 30% of severe hail events occurring in May. Nevertheless, severe hail events have a low occurrence in absolute terms and longer time series of observations are required to obtain a more reliable severe hail climatology. [ABSTRACT FROM AUTHOR]

ABSTRACT One of the main concerns with quantitative precipitation estimates ( QPE) based on weather radar observations is the extent to which nowcasters should believe them as they rush to issue warnings for dangerous weather phenomena that might endanger human lives and goods. This paper aims to improve QPE by adjusting the mean field bias using rain gauge measurements. Radar data used in this research were supplied from a single polarization S-band Doppler radar, WSR-98D (Weather Surveillance Radar - 98 Doppler), located almost in the centre of Romania, at Bobohalma, and a network consisting of 27 rain gauges within weather stations belonging to the Romanian National Meteorological Administration. The procedure consisted of two main steps: in step one, the reflectivity data were converted into rain rate using the Z- R relationship; in step two, differences between radar data and gauge data were investigated using four objective functions, the ratio between radar data and gauge data, the root-mean-square factor, and Pearson and Spearman correlations. The findings are consistent with previous studies, emphasizing that both the differences and correlations between radar data and rain gauge amounts have local significance rather than general relevance over the studied area. [ABSTRACT FROM AUTHOR]

ABSTRACT This study evaluated the gap filler radar as an implementation of the 1.5 km CAPPI (constant altitude plan position indicator) data in Korea. The use of the 1.5 km CAPPI data was an inevitable choice, given the topography of the Korean Peninsula and the location of the radar. However, there still exists a significant portion of beam blockage, and thus there has been debate about the need to introduce the gap filler radar (or, the gap-filler). This study evaluated the possible benefits of introducing gap-fillers over the Korean Peninsula. As a first step, the radar data error was quantified by G/ R ratio and root mean square error ( RMSE), and the radar data over the Korean Peninsula were evaluated. The gap-fillers were then located where the error was high, and their effect was then evaluated by the decrease in the G/ R ratio and RMSE. The mean values of the G/ R ratio and RMSE of the 1.5 km CAPPI data over the Korean Peninsula were estimated to be about 2.5 and 4.5 mm h-1, respectively. Even after the mean-field bias correction, the RMSE of the 1.5 km CAPPI data was not decreased and mostly remained very high, around 4.4 mm h-1. Unfortunately, the effect of the gap-filler on the 1.5 km CAPPI data was also found to be very small, just 1-2%. However, the gap-filler could be beneficial, if the lowest elevation angle data were used instead of the 1.5 km CAPPI data. The effect of five gap-fillers could be up to 7% decrease in RMSE. [ABSTRACT FROM AUTHOR]

ABSTRACT This study presents a new algorithm Fuzzy logic Algorithm for Storm Tracking ( FAST) for tracking convective cells in 3D Cartesian co-ordinates. A fuzzy logic approach was applied to track cells with objectively determined membership functions ( MFs) and weights that were derived from a large set of convective cell tracks (2326 pairs of convective cells). Three feature parameters (cell motion speed, area change ratio and axis transformation ratio) were selected by Bayesian information criterion and combined through fuzzy logic. FAST was evaluated with three skill scores using 2561 (2456) pairs of convective cells with (without) mergers and splits. The performance was tested in comparison with Thunderstorm Identification, Tracking, Analysis, and Nowcasting ( TITAN) as a baseline method. For reference tracks without merger and split, the critical success index ( CSI) of FAST reaches about 0.89. FAST shows better skill scores ( CSI = 0.92 and 0.89 at speed limitations of 16.1 m s−1 and 25.0 m s−1, respectively) than those of TITAN ( CSI = 0.89 and 0.82 at the same speed limitations) and is independent of the number of convective cells and the speed limitation. When mergers and splits are considered in the reference, the tracking performance of FAST ( FASTMS) with a merger and split process is always better than that of TITAN ( TITANMS) with a merger and split process and is independent of speed limitations. When FAST (= FASTnoMS) does not include a merger and split process, its tracking performance is better than that of TITANMS at speed limitation of > 20 m s−1. [ABSTRACT FROM AUTHOR]

In the present study, a rapid and simple approach is presented to rearrange long-term precipitation fields (i.e. periods⩾1 year) from a multi-site radar product, which were originally derived on an hourly basis as quantitative precipitation estimates (QPEs) for flood risk forecasting and management. The method consists of interquartile outlier detection and linear transformations to achieve a better representation of elevation above sea level (asl). The presented approach was deployed in the Free State of Saxony, Germany, for the period from April 2004 through December 2009. The results are compared with two independently derived and highly resolved gridded precipitation data sets (i.e. 1 km resolution). An algorithm based on the golden section search and parabolic interpolation was deployed to determine an optimal set of parameters. The results show a significant increase in quality in the long-term precipitation information in comparison to the original QPEs. Hence, a more reasonable order of the correlation and of the empirical histogram could be obtained after processing. The scorings within the means of the Taylor diagram show that the patterns of the compared data sets are very similar. It can be inferred that the results support the application of the introduced procedure for the adjustment of QPEs for periods of 1 year and greater because a more realistic appearance of the climatological precipitation field can be achieved; in addition, it is an approach that is simple and reliable. The results can be used for climatological analysis and should help in the application of radar products for improving station-based precipitation climatology. [ABSTRACT FROM AUTHOR]

With a focus on the properties of atmospheric thickness, temporal variations of various atmospheric quantities at isobaric levels measured and retrieved from microwave radiometer (MWR) and radiosonde soundings were investigated for precipitation events during the winter of 2011 near Seoul, Korea. Wind profiler analyses were performed additionally for examining the returned radar power signal and Doppler velocity characteristics of the vertical structure. It was found that there exists a lead period of 9 to 12 h that shows little change (almost no increase) in the 1000-850 hPa thickness before precipitation. Between snow and rain events, thermodynamic characteristics were similar at lower levels but were noticeably different at upper levels, showing their dependence on the cloud-top height and updraft intensity. Higher correlation in thickness and temperature between the MW Rand radiosonde data was found at lower levels than upper, suggesting that the lead period of the 1000-850 hPa thickness as shown by the MWR is reliable in this study. The vertical structure and precipitation properties analysed from wind profiler observations showed close agreement with MWR retrievals of precipitable water vapour and liquid water content, indicating the effectiveness of the wind profiler as a complementary tool in MWR studies. [ABSTRACT FROM AUTHOR]

The present study introduces a nowcast scheme that assimilates radar extrapolation data (RED) into a nowcasting version of the high resolution limited area model (HIRLAM) numerical weather prediction (NWP) model covering the area of Denmark. The RED are based on the Co-TREC (tracking radar echoes by correlation) methodology and are generated from cleaned radarmosaics from theDanish weather radar network. The assimilation technique is a newly developed method that increases model precipitation by increasing low-level convergence and decreasing convergence aloft in order to increase the vertical velocity. A selection criterion based on spatial coverage and temporal duration is applied to identify rain events for evaluation. Eight events from August 2010 comply with the criteria and additionally, an extreme event on 2 July 2011 is also evaluated. The RED assimilated NWP model is verified against a non-assimilated NWP reference run using the fractional skill score. In all cases, the predictability of the RED assimilated NWP model has been increased compared with the non-RED assimilated NWP model. After ending the RED assimilation, the positive effects continue until the skill converges towards the reference run. The level of improved predictability relies on the RED quality, which again relies on the type of event. [ABSTRACT FROM AUTHOR]

ABSTRACT The use of radar to provide rainfall estimates is becoming more attractive, especially with the number of rain-gauges in operation reducing year on year in many countries. However, while radars give very good spatial representations of rainfall, they tend to underestimate rainfall when compared to rain-gauges, for the selected area. To improve the accuracy of radar estimates, they are usually adjusted by comparing the radar and rain-gauge estimates. Most of the adjustment methods consist of either using a value that may or may not vary with distance from the radar, or by varying the Z-R relationship. With the exception to the methods that involve varying the Z-R relationship, the effects of rainfall intensity on the adjustment factor are ignored. Two new methods that take into account the variability in the intensity of rainfall are proposed: one that varies the adjustment value based on discrete intensity bins and the other varies continuously. The proposed method is shown to be better than uniform adjustment in both calibration and validation in split-sample tests. [ABSTRACT FROM AUTHOR]

ABSTRACT This work describes the implementation and validation of an objective methodology for storm cell identification and tracking, which only uses radar reflectivity data as input. This methodology was applied to the statistical characterization of Cuban convective storms. The properties of radar echoes during 13 convective days in the vicinity of Camaguey, Cuba for the 2007 rainy season are obtained, and frequency distributions of mean hour of occurrence, total duration, maximum reflectivity, area, top height, and mean velocity during each trajectory are presented and compared to similar reports in Cuba and other regions. The trajectory of each storm is extrapolated to forecast its position up to 1 h ahead. Both tracking and nowcasting are evaluated for different situations, showing the good performance of the method. Copyright © 2013 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

The propagation of electromagnetic waves emitted from ground-based meteorological radar is determined using high-resolution radiosonde data. In this work, a detailed analysis of surface ducts has been undertaken to determine the anomalous propagation days on the coastal site of Douala in Cameroon (4 °N, 9.7 °E) over the Gulf of Guinea. The median duct strength shows that the strongest ducts are seen in the rainy season and that the surface duct occurs more frequently during the day than at night. The duct strength seasonal variability shows a value over Douala of about − 7.2 M-units for 1200 UTC and − 4.5 M-units for 0000 UTC in January, whereas in July the duct strengths are stronger during the day (−12.8 M-units at 1200 UTC) than at night (−9.8 M-units at 0000 UTC). The Cloudsat data products for Douala and neighbouring areas during the days of 26 January 2009 at 233525 UTC and 5 July 2009 at 233552 UTC were also analysed in association with the statistical discussion. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

A set of quality control algorithms developed in the framework of the RADVOL-QC system is described. The algorithms are dedicated to 3-D radar reflectivity data (volumes) provided by a weather radar with single-polarization of the beam. The system consists of two paths of data processing: quality correction and quality characterization. Processing by means of data correction algorithms allows the reduction of uncertainty in the data, whereas quality characterization algorithms generate a map of quality index (QI) that can be attached to the data. The following correction algorithms are included in the proposed scheme: (1) removal of geometrically shaped non-meteorological echoes (from the Sun and other emitters); (2) removal of measurement noise (specks); (3) beam blockage correction, and, (4) attenuation in the rain correction. The list of quality characterization algorithms is longer, as it also includes evaluation of data uncertainty due to technical radar parameters, distance to radar related effects, and ground clutter. All the algorithms have been developed paying special attention to possibility of operational application. The system is planned to be extended successively by other quality factors. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

ABSTRACT Given the increasing interest in using radar-based rainfall estimates in hydrologic studies, efforts are critically needed to assess the applicability of recently-proposed methods that focus on quantitative modelling of radar-rainfall uncertainties. The current study reports on the implementation and assessment of an empirically-based approach (known as product-error driven (PED) method) for modelling uncertainties in radar-based rainfall products. In this study, the PED method is applied to a suite of operational radar-based products produced by the U.S. National Weather Service (NWS) Multi-Sensor Precipitation Estimator (MPE) algorithm. The tested MPE products range from a radar-only product, to other products that include various degrees of mean-field and local bias adjustments and gauge-radar optimal merging procedures. Data from an independent dense rain gauge cluster located in south-west Louisiana is used as a proxy for the unknown surface rainfall rates. The focus is on assessing the transferability of the PED across different radar-rainfall products and geographical regions, and the generality of the distributional and parametric assumptions of the PED method. The study also provides insight on the critical issue of the sampling variability and data requirements that govern the implementation, interpretation and possible future enhancements of the radar-error modelling methods. [ABSTRACT FROM AUTHOR]

The Urumqi Institute of Desert Meteorology under the China Meteorological Administration (CMA) launched an observational experiment with a boundary layer wind-profiling radar in Urumqi City in February to March 2010. As described in this paper, wind profiles from the experiment were used to first compare with radiosonde data measured simultaneously to verify the confidence of wind-profiling radar data, which was used to analyse the wind field characteristics and other related issues in a snowstorm process that took place in Urumqi on 28 March 2010. Both wind direction and speed derived from wind-profiling radar data have good consistency with those from radiosonde data (wind velocity coefficient: 0.85; wind direction coefficient: 0.78). Before the snowstorm, a surface wind shear occurred. After outbreak of the event, the local weather was dominated by northwesterly and northerly winds at increasing velocities, while temperature was dropping and air pressure increased. Through in-depth analyses of the time-height profiles of the snowstorm process, it can be determined on a preliminary basis that the snowstorm was induced by a cold front system that entered the area around 2200-2300 LST on 27 March. A shear line that developed from a lower to a higher altitude represented both height and location of the frontal zone, and the cold front motion was broadly in a northeast-southwest direction. The wind direction in the process changed counterclockwise with height: this was mainly governed by cold advection, and baroclinic instability was established soon after the overpass of the cold front, eventually leading to the snowstorm. During the weather event, the equivalent reflectivity factor Ze was consistent with the onset and end of the snowstorm, the height at which rain (snow) particles were formed and snowfall intensity. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

The errors introduced into radar estimates of rainfall by making observations at spatial and temporal resolutions that are coarse compared with precipitation systems' characteristic length and time scales are explored in this study. High resolution (200 m, 50 s) X-band radar data from 48 mid-latitude precipitation events are downgraded progressively in spatial and temporal resolution so that estimates of this sampling error can be made by comparing 10 min rainfall accumulations of this data to accumulations calculated from the original high resolution data. The analysis shows these errors to be of significant magnitude. For 2 km and 5 min sampling, this error varies from 17 to 64% of the mean rainfall accumulation. A relationship is shown between the error introduced from the reduction in spatial resolution and the characteristic length scale of the precipitation system along with a metric of precipitation intensity. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Emergency managers (EMs) play a critical role in communicating severe weather and tornado warnings to the public, yet communicating the uncertainty of when, where or if a tornado may hit remains a great challenge for EMs. Focus group and survey data concerning weather product usage, weather observing spotter interaction, and decisions to warn the public were collected from Oklahoma EMs in order to characterize the communication processes EMs employ during severe weather outbreaks. These processes include: (1) acquiring weather information, (2) interpreting the information in order to make weather hazard threat assessments, (3) verifying the information, and (4) making time-sensitive warning decisions. The results indicate that while EMs use a variety of weather and radar products to acquire information, weather observing spotters are key sources of verification data. With respect to warning the public about tornado threats, sirens are the primary method. These findings are related to the development of a new radar system being developed by the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA), so that this new technology can be designed to reduce uncertainty in the EM decision-making and warning communication processes. Copyright © 2010 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

The introduction of technological advancements in TV weather broadcasts has been a constant trend, particularly in the last 20 years. Rapidly evolving techniques, both from the point of view of new meteorological products or the use of advanced computer generated graphics, have offered a wide choice of possibilities to enhance information content and communication capability. However, despite the potential advantage of technological innovation, a case-by-case careful feasibility analysis is always required, including not only technical and economical aspects, but most importantly communication issues bearing in mind the education and acceptance of the audience. In this paper two specific cases of technological advances in TV weather broadcasts, seen from the perspective of the last 20 years, are examined: the introduction of weather radar images and the use of animated characters as weathercasters. These cases are illustrated from the experience acquired in TVC, the public Television of Catalonia (NE Spain). Direct access to weather radar data was first made possible within the framework of a joint collaboration agreement with the University of Barcelona and the Meteorological Service of Catalonia, which installed a new C-band Doppler radar. The use of animated characters as weathercasters was the result of developing and advanced human-like interface accessing an already existing database fed automatically by NWP forecasts. In each case a description of both technical aspects and also of the benefits and challenges seen from the communication point of view found in their introduction in TV weather broadcasts is provided. Copyright © 2010 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Radar has been applied to the study of insect migration for almost 40 years, but most entomological radars operate at Xband 9.4 GHz, 3.2 cm wavelength and can only detect individuals of relatively large species, such as migratory grasshoppers and noctuid moths, over all of their flight altitudes. Many insects including economically important species are much smaller than this, but development of the requisite higher power andor higher frequency radar systems to detect these species is often prohibitively expensive. In this paper, attention is focused upon the uses of some recentlydeployed meteorological sensing devices to investigate insect migratory flight behaviour, and especially its interactions with boundary layer processes. Records were examined from the verticallypointing 35 GHz ‘Copernicus’ and 94 GHz ‘Galileo’ cloud radars at Chilbolton Hampshire, England for 12 cloudless and convective occasions in summer 2003, and one of these occasions 13 July is presented in detail. Insects were frequently found at heights above aerosol particles, which represent passive tracers, indicating active insect movement. It was found that insect flight above the convective boundary layer occurs most often during the morning. The maximum radar reflectivity an indicator of aerial insect biomass was found to be positively correlated with maximum screen temperature. Copyright © 2009 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

This paper describes an experimental procedure for assimilating CloudSat Cloud Profiling Radar CPR observations in ALADIN 3DVar through the use of humidity pseudoobservations derived from a onedimensional Bayesian analysis. Cloud data are considered as binary occurrences ‘cloud’ vs‘nocloud’, which makes the approach feasible to be extended to other cloudiness observations, and to any other binary observation in general. A simple largescale condensation scheme is used for projecting the prior information from a Numerical Weather Prediction model into cloud fraction space. Verification over a 1 month assimilation test period indicates a clear benefit of the pseudoobservation assimilation scheme for the limited CloudSat CPR data set, especially in terms of improved skill scores for dynamical parameters such as geopotential and wind. Copyright © 2008 Royal Meteorological Society [ABSTRACT FROM AUTHOR]