Showing papers on "Weather radar published in 2018"

Survey of data assimilation methods for convective-scale numerical weather prediction at operational centres

Abstract: Data assimilation methods for convective-scale numerical weather prediction at operational centres are surveyed in this paper. The operational methods include variational methods (3D-Var and 4D-Var), ensemble methods (LETKF) and hybrids between variational and ensemble methods (3DEnVar and 4DEnVar). At several of the operational centres, other assimilation algorithms, like latent heat nudging, are additionally applied to improve the model initial state, with emphasis on convective scales. It is demonstrated that the quality of forecasts based on initial data from convective-scale data assimilation is significantly better than the quality of forecasts from simple downscaling of larger-scale initial data. The duration of positive impact depends however on the weather situation, the size of the computational domain and the data that are assimilated. It is furthermore shown that more-advanced methods applied at convective scales provide improvements compared to simpler methods. This motivates continued research and development in convective-scale data assimilation. Challenges in research and development for improvements of convective-scale data assimilation are also reviewed and discussed in this paper. The difficulty of handling the wide range of spatial and temporal scales makes development of multi-scale assimilation methods and space-time covariance localization techniques important. Improved utilization of observations is also important. In order to extract more information from existing observing systems of convective-scale phenomena, for example weather radar data and satellite image data, it is necessary to provide improved statistical descriptions of the observation errors associated with these observations.

Intensification of Convective Rain Cells at Warmer Temperatures Observed from High-Resolution Weather Radar Data

Abstract: This study contributes to the understanding of the relationship between air temperature and convection by analyzing the characteristics of rainfall at the storm and convective rain cell scales. High spatial–temporal resolution (1 km, 5 min) estimates from a uniquely long weather radar record (24 years) were coupled with near-surface air temperature over Mediterranean and semiarid regions in the eastern Mediterranean. In the examined temperature range (5°–25°C), the peak intensity of individual convective rain cells was found to increase with temperature, but at a lower rate than the 7%°C−1 scaling expected from the Clausius–Clapeyron relation, while the area of the individual convective rain cells slightly decreases or, at most, remains unchanged. At the storm scale, the areal convective rainfall was found to increase with warmer temperatures, whereas the areal nonconvective rainfall and the stormwide area decrease. This suggests an enhanced moisture convergence from the stormwide extent toward th...

Inter-Comparison of Rain-Gauge, Radar, and Satellite (IMERG GPM) Precipitation Estimates Performance for Rainfall-Runoff Modeling in a Mountainous Catchment in Poland

Abstract: Precipitation is one of the essential variables in rainfall-runoff modeling. For hydrological purposes, the most commonly used data sources of precipitation are rain gauges and weather radars. Recently, multi-satellite precipitation estimates have gained importance thanks to the emergence of Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG GPM), a successor of a very successful Tropical Rainfall Measuring Mission (TRMM) mission which has been providing high-quality precipitation estimates for almost two decades. Hydrological modeling of mountainous catchment requires reliable precipitation inputs in both time and space as the hydrological response of such a catchment is very quick. This paper presents an inter-comparison of event-based rainfall-runoff simulations using precipitation data originating from three different sources. For semi-distributed modeling of discharge in the mountainous river, the Hydrologic Engineering Center-Hydrologic Modelling System (HEC-HMS) is applied. The model was calibrated and validated for the period 2014–2016 using measurement data from the Upper Skawa catchment a small mountainous catchment in southern Poland. The performance of the model was assessed using the Nash–Sutcliffe efficiency coefficient (NSE), Pearson’s correlation coefficient (r), Percent bias (PBias) and Relative peak flow difference (rPFD). The results show that for the event-based modeling adjusted radar rainfall estimates and IMERG GPM satellite precipitation estimates are the most reliable precipitation data sources. For each source of the precipitation data the model was calibrated separately as the spatial and temporal distributions of rainfall significantly impact the estimated values of model parameters. It has been found that the applied Soil Conservation Service (SCS) Curve Number loss method performs best for flood events having a unimodal time distribution. The analysis of the simulation time-steps indicates that time aggregation of precipitation data from 1 to 2 h (not exceeding the response time of the catchment) provide a significant improvement of flow simulation results for all the models while further aggregation, up to 4 h, seems to be valuable only for model based on rain gauge precipitation data.

A Multi-Platform Hydrometeorological Analysis of the Flash Flood Event of 15 November 2017 in Attica, Greece

Abstract: Urban areas often experience high precipitation rates and heights associated with flash flood events. Atmospheric and hydrological models in combination with remote-sensing and surface observations are used to analyze these phenomena. This study aims to conduct a hydrometeorological analysis of a flash flood event that took place in the sub-urban area of Mandra, western Attica, Greece, using remote-sensing observations and the Chemical Hydrological Atmospheric Ocean Wave System (CHAOS) modeling system that includes the Advanced Weather Research Forecasting (WRF-ARW) model and the hydrological model (WRF-Hydro). The flash flood was caused by a severe storm during the morning of 15 November 2017 around Mandra area resulting in extensive damages and 24 fatalities. The X-band dual-polarization (XPOL) weather radar of the National Observatory of Athens (NOA) observed precipitation rates reaching 140 mm/h in the core of the storm. CHAOS simulation unveils the persistent orographic convergence of humid southeasterly airflow over Pateras mountain as the dominant parameter for the evolution of the storm. WRF-Hydro simulated the flood using three different precipitation estimations as forcing data, obtained from the CHAOS simulation (CHAOS-hydro), the XPOL weather radar (XPOL-hydro) and the Global Precipitation Measurement (GMP)/Integrated Multi-satellitE Retrievals for GPM (IMERG) satellite dataset (GPM/IMERG-hydro). The findings indicate that GPM/IMERG-hydro underestimated the flood magnitude. On the other hand, XPOL-hydro simulation resulted to discharge about 115 m3/s and water level exceeding 3 m in Soures and Agia Aikaterini streams, which finally inundated. CHAOS-hydro estimated approximately the half water level and even lower discharge compared to XPOL-hydro simulation. Comparing site-detailed post-surveys of flood extent, XPOL-hydro is characterized by overestimation while CHAOS-hydro and GPM/IMERG-hydro present underestimation. However, CHAOS-hydro shows enough skill to simulate the flooded areas despite the forecast inaccuracies of numerical weather prediction. Overall, the simulation results demonstrate the potential benefit of using high-resolution observations from a X-band dual-polarization radar as an additional forcing component in model precipitation simulations.

Field validation of radar systems for monitoring bird migration

Abstract: Advances in information technology are increasing the use of radar as a tool to investigate and monitor bird migration movements. We set up a field campaign to compare and validate outputs from different radar systems. Here we compare the pattern of nocturnal bird migration movements recorded by four different radar systems at a site in southern Sweden. Within the range of the weather radar (WR) Angelholm, we operated a "BirdScan" (BS) dedicated bird radar, a standard marine radar (MR), and a tracking radar (TR). The measures of nightly migration intensities, provided by three of the radars (WR, BS, MR), corresponded well with respect to the relative seasonal course of migration, while absolute migration intensity agreed reasonably only between WR and BS. Flight directions derived from WR, BS and TR corresponded very well, despite very different sample sizes. Estimated mean ground speeds differed among all four systems. The correspondence among systems was highest under clear sky conditions and at high altitudes. Synthesis and applications. While different radar systems can provide useful information on nocturnal bird migration, they have distinct strengths and weaknesses, and all require supporting data to allow for species level inference. Weather radars continuously detect avian biomass flows across a wide altitude band, making them a useful tool for monitoring and predictive applications at regional to continental scales that do not rely on resolving individuals. BirdScan and marine radar's strengths are in local and low altitude applications, such as collision risks with man-made structures and airport safety, although marine radars should not be trusted for absolute intensities of movement. In quantifying flight behaviour of individuals, tracking radars are the most informative. (Less)

China's 1 km Merged Gauge, Radar and Satellite Experimental Precipitation Dataset

Abstract: Based on high-density gauge precipitation observations, high-resolution weather radar quantitative precipitation estimation (QPE) and seamless satellite-based precipitation estimates, a 1-km experimental gauge-radar-satellite merged precipitation dataset has been developed using the proposed local gauge correction (LGC) and optimal interpolation (OI) merging strategies. First, hourly precipitation analyses from approximately 40,000 automatic weather stations at 0.01° resolution were used to correct bias in the radar QPE Group System (QPEGS), developed by the China Meteorological Administration (CMA) and the Climate Prediction Center Morphing (CMORPH) precipitation products. As precipitation events tend to have a more localized distribution at the hourly and 0.01° resolutions, three core parameters were improved using the OI method. (a) The spatial dependence of the error variance for radar QPE was accounted for over six sub-regions in China and is shown as a non-linear function of the gauge precipitation analysis. (b) The spatial dependence of error correlation for the radar QPE decreased exponentially with distance. (c) The error of the hourly gauge-based precipitation analysis was quantified as a function of the precipitation amount and the gauge network density, using the Monte Carlo method to randomly sample the gauge observations over the dense gauge network. The performance of the 1-km experimental gauge-radar-satellite merged precipitation dataset (named as China Merged Precipitation Analysis: CMPA_1km) was assessed at 6 h-temporal resolutions and 0.03° × 0.03° spatial resolution using precipitation observations from 208 independent hydrological stations as a reference. Compared with radar QPE and CMORPH, the CMPA-1km showed obviously better accuracy in all sub-regions and during all seasons. In contrast, gauge analysis and CMPA-1km shared similar accuracy, but the latter could estimate heavy precipitation more accurately than the former, as well as the latter has the advantage of seamless spatial coverage. However, the CMPA-1km exhibits larger uncertainty during the cold season compared to the warm season, which will need further improvement in future work. The downscaled bias-corrected 0.01° resolution CMORPH was employed to fill the gaps in regions, mainly in Western China and the Tibetan Plateau, where gauge and radar measurements are limited.

Fast-Scanning Phased-Array Weather Radar With Angular Imaging Technique

Abstract: A phased-array weather radar (PAWR) with fast scanning and wide elevation coverage capabilities has been developed. The PAWR transmits elevationally broad beams by feeding power to a limited number of its antenna elements, and receives signals reflected by precipitation media using all 128 antenna elements, each of which is connected to an analog-to-digital converter (ADC). After ADC sampling, digital processing of beamforming is applied to the received signals to accomplish receptions simultaneously over multiple angles. The PAWR can, thereby, make observations at 100-m range increments out to 60 km at 1° azimuth angle intervals over a range of 360° and at about 1° elevation-angle intervals from 0°–90° within the short time of 30 s. Reflectivity factors measured by the PAWR were compared with those from a collocated C-band radar, and were found to have a bias of 0.53 dB and a standard deviation of 3.68 dB, which indicates sufficient accuracy for observing precipitation. Furthermore, in a sample observation of convective rain, the PAWR detected a precipitation core 9 min before it reached the ground by its 30-s fast scanning and wide elevation coverage. It, thereby, became evident that the fast scanning and wide elevation coverage capabilities of the PAWR give earlier detection and a higher detection probability, which will enable earlier and more accurate warnings of severe weather phenomena and a better potential of mitigating the damage.

Comparison of the GPM IMERG Final Precipitation Product to RADOLAN Weather Radar Data over the Topographically and Climatically Diverse Germany

Abstract: Precipitation measurements provide crucial information for hydrometeorological applications. In regions where typical precipitation measurement gauges are sparse, gridded products aim to provide alternative data sources. This study examines the performance of NASA’s Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement Mission (IMERG, GPM) satellite precipitation dataset in capturing the spatio-temporal variability of weather events compared to the German weather radar dataset RADOLAN RW. Besides quantity, also timing of rainfall is of very high importance when modeling or monitoring the hydrologic cycle. Therefore, detection metrics are evaluated along with standard statistical measures to test both datasets. Using indices like “probability of detection” allows a binary evaluation showing the basic categorical accordance of the radar and satellite data. Furthermore, a pixel-by-pixel comparison is performed to assess the ability to represent the spatial variability of rainfall and precipitation quantity. All calculations are additionally carried out for seasonal subsets of the data to assess potentially different behavior due to differences in precipitation schemes. The results indicate significant differences between the datasets. Overall, GPM IMERG overestimates the quantity of precipitation compared to RADOLAN, especially in the winter season. Moreover, shortcomings in detection performance arise in this season with significant erroneously-detected, yet also missed precipitation events compared to the weather radar data. Additionally, along secondary mountain ranges and the Alps, topographically-induced precipitation is not represented in GPM data, which generally shows a lack of spatial variability in rainfall and snowfall estimates due to lower resolution.

Spatial variability in snow precipitation and accumulation in COSMO–WRF simulations and radar estimations over complex terrain

Abstract: . Snow distribution in complex alpine terrain and its evolution in the future climate is important in a variety of applications including hydropower, avalanche forecasting and freshwater resources. However, it is still challenging to quantitatively forecast precipitation, especially over complex terrain where the interaction between local wind and precipitation fields strongly affects snow distribution at the mountain ridge scale. Therefore, it is essential to retrieve high-resolution information about precipitation processes over complex terrain. Here, we present very-high-resolution Weather Research and Forecasting model (WRF) simulations (COSMO–WRF), which are initialized by 2.2 km resolution Consortium for Small-scale Modeling (COSMO) analysis. To assess the ability of COSMO–WRF to represent spatial snow precipitation patterns, they are validated against operational weather radar measurements. Estimated COSMO–WRF precipitation is generally higher than estimated radar precipitation, most likely due to an overestimation of orographic precipitation enhancement in the model. The high precipitation amounts also lead to a higher spatial variability in the model compared to radar estimates. Overall, an autocorrelation and scale analysis of radar and COSMO–WRF precipitation patterns at a horizontal grid spacing of 450 m show that COSMO–WRF captures the spatial variability normalized by the domain-wide variability in precipitation patterns down to the scale of a few kilometers. However, simulated precipitation patterns systematically show a lower variability on the smallest scales of a few hundred meters compared to radar estimates. A comparison of spatial variability for different model resolutions gives evidence for an improved representation of local precipitation processes at a horizontal resolution of 50 m compared to 450 m. Additionally, differences of precipitation between 2830 m above sea level and the ground indicate that near-surface processes are active in the model.

Unraveling hydrometeor mixtures in polarimetric radar measurements

Abstract: . Radar-based hydrometeor classification typically comes down to determining the dominant type of hydrometeor populating a given radar sampling volume. In this paper we address the subsequent problem of inferring the secondary hydrometeor types present in a volume – the issue of hydrometeor de-mixing. The present study relies on the semi-supervised hydrometeor classification proposed by Besic et al. ( 2016 ) but nevertheless results in solutions and conclusions of a more general character and applicability. In the first part, oriented towards synthesis, a bin-based de-mixing approach is proposed, inspired by the conventional coherent and linear decomposition methods widely employed across different remote-sensing disciplines. Intrinsically related to the concept of entropy, introduced in the context of the radar hydrometeor classification in Besic et al. ( 2016 ) , the proposed method, based on the hypothesis of the reduced random interferences of backscattered signals, estimates the proportions of different hydrometeor types in a given radar sampling volume, without considering the neighboring spatial context. Plausibility and performances of the method are evaluated using C- and X-band radar measurements, compared with hydrometeor properties derived from a Multi-Angle Snowflake Camera instrument. In the second part, we examine the influence of the potential residual random interference contribution in the backscattering from different hydrometeors populating a radar sampling volume. This part consists in adapting and testing the techniques commonly used in conventional incoherent decomposition methods to the context of weather radar polarimetry. The impact of the residual incoherency is found to be limited, justifying the hypothesis of the reduced random interferences even in a case of mixed volumes and confirming the applicability of the proposed bin-based approach, which essentially relies on first-order statistics.

A Method of Weather Radar Echo Extrapolation Based on Convolutional Neural Networks

Abstract: Weather radar echo extrapolation techniques possess wide application prospects in short-term forecasting (i.e., nowcasting). Traditional methods of radar echo extrapolation have difficulty obtaining long limitation period data and lack the utilization rate of radar. To solve this problem, this paper proposes a method of weather radar echo extrapolation based on convolutional neural networks (CNNs). To create a strong correlation among contiguous weather radar echo images from traditional CNNs, this method present a new CNN model: Recurrent Dynamic CNNs (RDCNN). RDCNN consists of a recurrent dynamic sub-network and a probability prediction layer, which constructs a cyclic structure in the convolution layer, improving the ability of RDCNN to process time-related images. Nanjing, Hangzhuo and Xiamen experimented with radar data, and compared with traditional methods, our method achieved higher accuracy of extrapolation and extended the limitation period effectively, meeting the requirements for application.

Influence of Disdrometer Type on Weather Radar Algorithms from Measured DSD: Application to Italian Climatology

Abstract: Relations for retrieving precipitation and attenuation information from radar measurements play a key role in radar meteorology. The uncertainty in such relations highly affects the precipitation and attenuation estimates. Weather radar algorithms are often derived by applying regression methods to precipitation measurements and radar observables simulated from datasets of drop size distributions (DSD) using microphysical and electromagnetic assumptions. DSD datasets can be derived from theoretical considerations or obtained from experimental measurements collected throughout the years by disdrometers. Although the relations obtained from experimental disdrometer datasets can be generally considered more representative of a specific climatology, the measuring errors, which depend on the specific type of disdrometer used, introduce an element of uncertainty to the final retrieval algorithms. Eventually, data quality checks and filtering procedures applied to disdrometer measurements play an important role. In this study, we pursue two main goals: (i) evaluate two different techniques for establishing weather radar algorithms from measured DSD, and (ii) investigate to what extent dual-polarization radar algorithms derived from experimental DSD datasets are influenced by the different error structures introduced by the various disdrometer types (namely 2D video disdrometer, first and second generation of OTT Parsivel disdrometer, and Thies Clima disdrometer) used to collect the data. Furthermore, weather radar algorithms optimized for Italian climatology are presented and discussed.

Statistics-based optimization of the polarimetric radar hydrometeor classification algorithm and its application for a squall line in South China

Abstract: A modified hydrometeor classification algorithm (HCA) is developed in this study for Chinese polarimetric radars. This algorithm is based on the U.S. operational HCA. Meanwhile, the methodology of statistics-based optimization is proposed including calibration checking, datasets selection, membership functions modification, computation thresholds modification, and effect verification. Zhuhai radar, the first operational polarimetric radar in South China, applies these procedures. The systematic bias of calibration is corrected, the reliability of radar measurements deteriorates when the signal-to-noise ratio is low, and correlation coefficient within the melting layer is usually lower than that of the U.S. WSR-88D radar. Through modification based on statistical analysis of polarimetric variables, the localized HCA especially for Zhuhai is obtained, and it performs well over a one-month test through comparison with sounding and surface observations. The algorithm is then utilized for analysis of a squall line process on 11 May 2014 and is found to provide reasonable details with respect to horizontal and vertical structures, and the HCA results—especially in the mixed rain-hail region—can reflect the life cycle of the squall line. In addition, the kinematic and microphysical processes of cloud evolution and the differences between radar-detected hail and surface observations are also analyzed. The results of this study provide evidence for the improvement of this HCA developed specifically for China.

A 15-year hail streak climatology for the Alpine region

Abstract: In this study, we present a unique 15-year hail streak climatology for Switzerland based on volumetric radar reflectivity. Two radar-based hail detection products and an automatic thunderstorm-tracking algorithm were reprocessed for the Extended convective season (April–September) between 2002 and 2016. More than 1.1 Million convective cells were automatically tracked over the full radar domain, and over 191,000 storms and 31,000 hail streaks in the considered subdomain were selected for analysis following consistency and robustness tests. The year-to-year variability in t h e number of hailstorms reveals two types of convective seasons: (a) a few seasons with hail frequency far above the average, and (b) all other years with an average number of hailstorms. A high number of hailstorms in a particular year is not correlated with a higher number of convective storms in general, but is related to a greater fraction of severe storms. Convection initiation, hail initiation, and hail frequency maxima are located along the southern and northern foothills over the pre-Alpine area and over th e Jura mountains. Few hail streaks are present over the Alpine main ridge. Hail streak frequency and location is found to be strongly dependent on the synoptic-scale weather regimes. This is important for monthly and seasonal outlooks, as well as for climate modelling. Analysis of storm life cycles shows that: (a) the majority of hail swaths contain only a single hail streak, (b) severe storms follow a more rapid evolution during their initial stages than do less severe storms, and (c) severe storms produce more spatially extended hail streaks. Finally, significant seasonal and diurnal cycles are present in most of the considered storm characteristics.

Analysis of Livorno Heavy Rainfall Event: Examples of Satellite-Based Observation Techniques in Support of Numerical Weather Prediction

Abstract: This study investigates the value of satellite-based observational algorithms in supporting numerical weather prediction (NWP) for improving the alert and monitoring of extreme rainfall events. To this aim, the analysis of the very intense precipitation that affected the city of Livorno on 9 and 10 September 2017 is performed by applying three remote sensing techniques based on satellite observations at infrared/visible and microwave frequencies and by using maps of accumulated rainfall from the weather research and forecasting (WRF) model. The satellite-based observational algorithms are the precipitation evolving technique (PET), the rain class evaluation from infrared and visible observations (RainCEIV) technique and the cloud classification mask coupling of statistical and physics methods (C-MACSP). Moreover, the rain rates estimated by the Italian Weather Radar Network are also considered to get a quantitative evaluation of RainCEIV and PET performance. The statistical assessment shows good skills for both the algorithms (for PET: bias = 1.03, POD = 0.76, FAR = 0.26; for RainCEIV: bias = 1.33, POD = 0.77, FAR = 0.41). In addition, a qualitative comparison among the three technique outputs, rain rate radar maps, and WRF accumulated rainfall maps is also carried out in order to highlight the advantages of the different techniques in providing real-time monitoring, as well as quantitative characterization of rainy areas, especially when rain rate measurements from Weather Radar Network and/or from rain gauges are not available.

Wildfire and Weather Radar: A Review

Abstract: Research in the pursuit of better understanding of fire behavior and fire-atmosphere interaction has frequently encountered a dearth of observational data, especially from events that cause most impact. Here we show that meteorological radar has been demonstrated as an effective tool for profiling the microphysics, thermodynamics, and fire behavior feedback of wildfire plumes, including for cases with deep and moist convection occurring in the fire plume. A synthesis of knowledge on the use of radar for the analysis of wildfire is presented, and the new term pyrometeor is introduced to describe the range of scatterers observed by radar, the reflectivity signature of which is determined by interacting processes of wildfire behavior and atmospheric convection. The reflectivity theories of pyrometeors are compared, and it is shown that there are gaps in knowledge on the size distributions of pyrometeors as well as the complex dielectrics. Observational case studies are compared across plume microphysics, plume thermodynamics and deep pyroconvection, and operational usage of radar to monitor wildfire. The dominant hypothesis of reflectivity is scattering from ash particles, though theories for scattering such as from larger debris exist, although evidence is limited for any hypothesis. Vortices have also been identified using Doppler velocity radar data, but there is limited understanding of their cause and influence on fire-atmosphere interactions. Recommendations are provided for methods and data sets to advance the application of radar for observing and understanding wildfires, including for plume microphysics and atmosphere-fire interactions.

Advancing Precipitation Estimation and Streamflow Simulations in Complex Terrain with X-Band Dual-Polarization Radar Observations

Abstract: In mountain basins, the use of long-range operational weather radars is often associated with poor quantitative precipitation estimation due to a number of challenges posed by the complexity of terrain. As a result, the applicability of radar-based precipitation estimates for hydrological studies is often limited over areas that are in close proximity to the radar. This study evaluates the advantages of using X-band polarimetric (XPOL) radar as a means to fill the coverage gaps and improve complex terrain precipitation estimation and associated hydrological applications based on a field experiment conducted in an area of Northeast Italian Alps characterized by large elevation differences. The corresponding rainfall estimates from two operational C-band weather radar observations are compared to the XPOL rainfall estimates for a near-range (10–35 km) mountainous basin (64 km2). In situ rainfall observations from a dense rain gauge network and two disdrometers (a 2D-video and a Parsivel) are used for ground validation of the radar-rainfall estimates. Ten storm events over a period of two years are used to explore the differences between the locally deployed XPOL vs. longer-range operational radar-rainfall error statistics. Hourly aggregate rainfall estimates by XPOL, corrected for rain-path attenuation and vertical reflectivity profile, exhibited correlations between 0.70 and 0.99 against reference rainfall data and 21% mean relative error for rainfall rates above 0.2 mm h−1. The corresponding metrics from the operational radar-network rainfall products gave a strong underestimation (50–70%) and lower correlations (0.48–0.81). For the two highest flow-peak events, a hydrological model (Kinematic Local Excess Model) was forced with the different radar-rainfall estimations and in situ rain gauge precipitation data at hourly resolution, exhibiting close agreement between the XPOL and gauge-based driven runoff simulations, while the simulations obtained by the operational radar rainfall products resulted in a greatly underestimated runoff response.

Nonlinear estimation of short time precipitation using weather radar and surface observations

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Latent Heat Nudging in the Canadian Regional Deterministic Prediction System

Abstract: This study reports on the progress toward operational weather radar data assimilation in Canada. As a first step, the latent heat nudging (LHN) technique has been tested for a period of 1 m...

Multifunction phased array radar advanced technology demonstrator nearfield test results

Abstract: Since 2007, MIT Lincoln Laboratory (LL) has been developing low-cost phased array panel technology in support of the Multifunction Phased Array Radar (MPAR) program. The MPAR program targeted the development of affordable active electronically scanned arrays (AESAs) for the civilian applications of aircraft surveillance and weather forecasting. Over the last decade, in a teaming with MACOM, Lincoln Laboratory has developed an S-band, 64-element dual-polarization (pol) panel with a peak radiated power of 6W per element per pol. The 3rd generation (Gen3) panel will be used to populate a 4m-diamter, 76-panel fully polarimetric AESA radar. This radar, the MPAR Advanced Technology Demonstrator (ATD), will be fielded at the National Weather Radar Testbed (NWRT) at the National Severe Storm Laboratory (NSSL) in Norman, OK, with IOC scheduled for 2018. This paper summarizes the development of the MPAR panels, the ATD, and the nearfield characterization of the ATD scheduled for the latter half of 2017.