Numerical Forecast Models Research Articles

An Observational Study of the Effects of Dry Air Produced in Dissipating Convective Storms on the Predictability of Severe Weather

Abstract This paper documents features that led to major forecast errors on the 12–24-h time scale in the nature and location of severe weather in the southern plains on 30 May 2012. Evidence is presented that the forecast errors were the result of 1) dry air that originated in a region of dissipating, elevated convective storms, and which was advected in a narrow tongue into western Oklahoma, inhibiting convective initiation; 2) the development of a cyclone along the dryline in western Texas, to the east of which several supercells formed; 3) the upscale development of the supercells into a mesoscale convective system (MCS) at nightfall; and 4) the dissipation of an MCS that had formed along a cold front in southwestern Kansas and was propagating into northwestern Oklahoma, as it encountered dry, subsiding air underneath the stratiform precipitation region of the rear portion of the MCS farther south. There was a meridionally oriented swath of high winds in clear air, in between the two MCSs. This swath of high winds may have been associated with a bore triggered at night by the MCSs approaching from the north, as the MCS collapsed, producing a gust front that propagated through stable, low-level air. This case study illustrates how the predictability of severe weather in a region can be extremely sensitive to the details of where nearby convective storms form and how they evolve. It also highlights the likely importance of the accurate representation of cloud microphysics and dynamics in numerical forecast models on predictability.

Integrating Weather Research and Forecasting Model, Noah Land Surface Model and Urban Canopy Model for Urban Heat Island Effect Assessment

Despite increased interest on the urban heat island (UHI) phenomenon, there are limited UHI studies on cities built using the green-city concept of Sir Ebenezer Howard [1]. The administrative capital of Malaysia, Putrajaya is one of such cities built using the green-city concept. The objective of this study was to confirm the effectiveness of the green city concept using the National Centre for Atmospheric Research (NCAR) numerical technique. Numerical mesoscale Weather Research and Forecasting (WRF) Model was coupled with Noah land surface model and a single layer urban canopy model (UCM) to investigate the existence and distribution of UHI, and the behavior of urban canopy layer (2-m) temperature of Putrajaya city. Few studies have been conducted using the NCAR numerical technique (WRF) to explore Malaysian climatology. Suitability of the model employed in studying UHI phenomenon of Putrajaya city was determined using in-situ study of the area, and observational data from AlamSekitar Malaysia SdnBhd (ASMA). Contribution of urban fabrics on the spatial and temporal variations of UHI was also investigated. Comparison with ASMA and in-situ data revealed a satisfactory performance of the model.UHI intensity (UHII) of Putrajaya exhibits a diurnal profile; increasing during the night to a peak value and then diminishing towards morning with a negligible value in the mid-day. In the night time, the UHII ranges from Original Research Article

Sampling error of observation impact statistics

An observation impact is an estimate of the forecast error reduction by assimilating observations with numerical model forecasts. This study compares the sampling errors of the observation impact statistics (OBIS) of July 2011 and January 2012 using two methods. One method uses the random error under the assumption that the samples are independent, and the other method uses the error with lag correlation under the assumption that the samples are correlated with each other. The OBIS are obtained using the forecast sensitivity to observation (FSO) tool in the Korea Meteorological Administration (KMA) unified model (UM). To verify the self-correlation of the observation impact data, the lag correlations of the observation impact data at 00 UTC in the Northern Hemisphere (NH) summer months (June, July and August 2011) and winter months (December 2011 and January and February 2012) are calculated. The self-correlation approaches zero at 6 days for the summer, whereas it approaches zero at 4 days for the winter, which implies that the observation impact data are serially correlated. The sampling error considering lag correlation is larger than the random error for NH summer and winter. While the random sampling error is approximately 12–13% of the approximation error, the sampling error considering the lag correlation is approximately half of the approximation error of the OBIS. The sampling error that considers the lag correlation of the OBIS is more appropriate for representing the uncertainty in the OBIS because the OBIS at different times are correlated.

Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM), and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro) and NFIS-WPM (Ave) are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.

Combining earthquake forecasts using differential probability gains

We describe an iterative method to combine seismicity forecasts. With this method, we produce the next generation of a starting forecast by incorporating predictive skill from one or more input forecasts. For a single iteration, we use the differential probability gain of an input forecast relative to the starting forecast. At each point in space and time, the rate in the next-generation forecast is the product of the starting rate and the local differential probability gain. The main advantage of this method is that it can produce high forecast rates using all types of numerical forecast models, even those that are not rate-based. Naturally, a limitation of this method is that the input forecast must have some information not already contained in the starting forecast. We illustrate this method using the Every Earthquake a Precursor According to Scale (EEPAS) and Early Aftershocks Statistics (EAST) models, which are currently being evaluated at the US testing center of the Collaboratory for the Study of Earthquake Predictability. During a testing period from July 2009 to December 2011 (with 19 target earthquakes), the combined model we produce has better predictive performance - in terms of Molchan diagrams and likelihood - than the starting model (EEPAS) and the input model (EAST). Many of the target earthquakes occur in regions where the combined model has high forecast rates. Most importantly, the rates in these regions are substantially higher than if we had simply averaged the models.

A case study of sea breeze blocking regulated by sea surface temperature along the English south coast

Abstract. The sensitivity of sea breeze structure to sea surface temperature (SST) and coastal orography is investigated in convection-permitting Met Office Unified Model simulations of a case study along the south coast of England. Changes in SST of 1 K are shown to significantly modify the structure of the sea breeze immediately offshore. On the day of the case study, the sea breeze was partially blocked by coastal orography, particularly within Lyme Bay. The extent to which the flow is blocked depends strongly on the static stability of the marine boundary layer. In experiments with colder SST, the marine boundary layer is more stable, and the degree of blocking is more pronounced. Although a colder SST would also imply a larger land–sea temperature contrast and hence a stronger onshore wind – an effect which alone would discourage blocking – the increased static stability exerts a dominant control over whether blocking takes place. The implications of prescribing fixed SST from climatology in numerical weather prediction model forecasts of the sea breeze are discussed.

Evaluation of statistical bias correction methods for numerical weather prediction model forecasts of maximum and minimum temperatures

Statistical bias correction methods for numerical weather prediction (NWP) forecasts of maximum and minimum temperatures over India in the medium-range time scale (up to 5 days) are proposed in this study. The objective of bias correction is to minimize the systematic error of the next forecast using bias from past errors. The need for bias corrections arises from the many sources of systematic errors in NWP modeling systems. NWP models have shortcomings in the physical parameterization of weather events and have the inability to handle sub-grid phenomena successfully. The statistical algorithms used for minimizing the bias of the next forecast are running-mean (RM) bias correction, best easy systematic estimator, simple linear regression and the nearest neighborhood (NN) weighted mean, as they are suitable for small samples. Bias correction is done for four global NWP model maximum and minimum temperature forecasts. The magnitude of the bias at a grid point depends upon geographical location and season. Validation of the bias correction methodology is carried out using daily observed and bias-corrected model maximum and minimum temperature forecast over India during July–September 2011. The bias-corrected NWP model forecast generally outperforms direct model output (DMO). The spatial distribution of mean absolute error and root-mean squared error for bias-corrected forecast over India indicate that both the RM and NN methods produce the best skill among other bias correction methods. The inter-comparison reveals that statistical bias correction methods improve the DMO forecast in terms of accuracy in forecast and have the potential for operational applications.

Meteotsunami forecasting: sensitivities demonstrated by the 2008 Boothbay, Maine, event

To support development of a meteotsunami forecasting capability for the USA, the National Oceanic and Atmospheric Administration funded a project in 2011 focused on meteotsunami forecasting for the US east coast. Meteotsunami forecasting shares many similarities with traditional tsunami forecasting, though the characterization and integration of the source with numerical forecast models is much different. Given meteotsunami source characterization through atmospheric observations and models, it is conceivable that meteotsunami alerts could be issued and their impact forecasted using existing tsunami forecast models with high-resolution coastal definition. To test this, the 2008 Boothbay, Maine, meteotsunami is simulated using an atmospheric source consisting of a moving pressure disturbance coupled with a tsunami forecast model. Sensitivities of the modeled impact to the source characteristics, such as speed, wavelength, and direction, are also tested. Results show that the observed impact can be re-created through numerical modeling when the pressure disturbance period is roughly matched with the harbor resonance and observed meteotsunami period.

"ALADIN" WEATHER MODEL LOCAL REVISIONS USING THE DIFFERENTIAL POLYNOMIAL NEURAL NETWORK

The 48-hour Aladin forecast model can predict significant meteoro- logical quantities in a middle scale area. Neural networks could try to replace some statistical techniques designed to adapt a global meteorological numerical forecast model for local conditions, described with real data surface observations. They succeed commonly a cut above problem solutions with a predefined testing data set, which provides bearing inputs for a trained model. Time-series predictions of the very complex and dynamic weather system are sophisticated and not any time faithful using simple neural network models entered only some few variables of their own next-time step estimations. Predicted values of a global meteorologi- cal forecast might instead enter a neural network locally trained model, for refine it. Differential polynomial neural network is a new neural network type developed by the author; it constructs and substitutes for an unknown general sum partial differential equation of a system description, with a total sum of fractional poly- nomial derivative terms. This type of non-linear regression is based on trained generalized data relations, decomposed into many partial derivative specifications. The characteristics of composite differential equation solutions of this indirect type of a function description can facilitate a much greater variety of model forms than is allowed using standard soft-computing methods. This adjective derivative model type is supposed to be able to solve much more complex problems than is usual using standard neural network techniques.

Evaluation of Various Surface Wind Products with OceanSITES Buoy Measurements

Abstract To facilitate evaluation and monitoring of numerical weather prediction model forecasts and satellite-based products against high-quality in situ observations, a data repository for collocated model forecasts, a satellite product, and in situ observations has been created under the support of various World Climate Research Program (WCRP) working groups. Daily measurements from 11 OceanSITES buoys are used as the reference dataset to evaluate five ocean surface wind products (three short-range forecasts, one reanalysis, and one satellite based) over a 1-yr intensive analysis period, using the WCRP community weather prediction model evaluation metrics. All five wind products correlate well with the buoy winds with correlations above 0.76 for all 11 buoy stations except the meridional wind at four stations, where the satellite and model performances are weakest in estimating the meridional wind (or wind direction). The reanalysis has higher cross-correlation coefficients (above 0.83) and smaller root-mean-square (RMS) errors than others. The satellite wind shows larger variability than that observed by buoys; contrarily, the models underestimate the variability. For the zonal and meridional winds, although the magnitude of biases averaged over all the stations are mostly <0.12 m s−1 for each product, the magnitude of biases at individual stations can be >1.2 m s−1, confirming the need for regional/site analysis when characterizing any wind product. On wind direction, systematic negative (positive) biases are found in the central (east central) Pacific Ocean. Wind speed and direction errors could induce erroneous ocean currents and states from ocean models driven by these products. The deficiencies revealed here are useful for product and model improvement.

Extreme Rapid Intensification of Typhoon Vicente (2012) in the South China Sea

Abstract One of the primary challenges for both tropical cyclone (TC) research and forecasting is the problem of intensity change. Accurately forecasting TC rapid intensification (RI) is particularly important to interests along coastlines and shipping routes, which are vulnerable to storm surge and heavy seas induced by intense tropical cyclones. One particular RI event in the western North Pacific Ocean with important scientific implications is the explosive deepening of Typhoon Vicente (2012). Vicente underwent extreme RI in the northern South China Sea just prior to landfall west of Hong Kong, China, with maximum sustained winds increasing from 50 kt (1 kt = 0.51 m s−1) at 0000 UTC 23 July to 115 kt at 1500 UTC 23 July. This increase of 65 kt in 15 h far exceeds established thresholds for TC RI. Just prior to this RI episode, Vicente exhibited a near-90° poleward track shift. The relationship between the track and intensity change is described, and the authors speculate that the passage of an upper-tropospheric (UT) “inverted” trough was a significant influence. An analysis of real-time numerical model guidance is provided and is discussed from an operational perspective, and high-resolution global model analyses are evaluated. Numerical model forecasts of the UT trough interaction with the TC circulation were determined to be a shortcoming that contributed to the intensity prediction errors for Vicente. This case study discusses the importance of considering UT features in TC intensity forecasting and establishes current modeling capabilities for future research.

The impact of raindrop drift in a three-dimensional wind field on a radar–gauge rainfall comparison

There are many causes for the discrepancies between weather radar and rain gauges, and among these, displacement of raindrops due to wind drift – which is especially a problem with high-spatial resolution weather radar – is largely ignored in the published literature. This is mainly due to the lack of high-resolution three-dimensional wind fields and feasible treatment of the raindrop size distribution (DSD). In this study, a new systematic approach is proposed to explore the radar–gauge relationship under the wind influence. The mass-weighted mean diameter of raindrops is derived for each radar grid from the DSD data. The reanalysis project ERA-40 data of the European Centre for Medium-range Weather Forecasts (ECMWF) are used to drive the numerical weather research and forecasting (WRF) model to generate high-resolution hourly three-dimensional wind fields. Trajectories and displacements of raindrops are then computed using a three-dimensional motion equation from the given radar beam height to the ground surface. Based on the radar rainfall surface interpolated by the bicubic spline method, the correlation of the radar–gauge pairs is used to validate the results. A case study with 20 storm events in the Brue catchment in South West England is chosen to evaluate the proposed scheme. It has been found that when wind drift is taken into account, the correlation coefficient in hourly gauge–radar comparisons can be enhanced by up to 30% and the average correlation coefficient for an event can be improved by 10%. However, there are still some situations in which the scheme fails to work, indicating the complexity and uncertainties in tackling this challenging problem. Further studies are needed to explore why those cases cause problems to the scheme and how it could be improved to cope with them.

Solutions for Improving the Radar Refractivity Measurement by Taking Operational Constraints into Account

Abstract Atmospheric refractivity depends on meteorological parameters such as temperature, water vapor pressure, and air pressure and can be measured using weather radar. This could be useful for convection prediction through the assimilation by numerical forecasting models in the boundary layer, particularly in prestorm conditions. However, this measurement is highly sensitive to phase ambiguities, induced by signal undersampling during rapid atmospheric fluctuations because of strong turbulent fluxes in the boundary layer or during extreme events. The refractivity measurement has been recently implemented on some radars of the French Application Radar à la Météorologie Infra-Synoptique (ARAMIS) network, which is composed of three different frequencies (S, C, and X bands). In view of operational applications, investigations are performed to improve the measurement and limit the phase ambiguity rate. The first recommendation is to decrease the time interval between two measurements by increasing the antenna speed rotation or by the use of a higher elevation angle. These methods lead to decreased sensitivity of the refractivity to the phase aliasing. The second recommendation is to improve the information from ground target by combining the two polarization radar returns and using shorter pulse width. These two different approaches, based on the radar capacity and the description of the target, are complementary and noticeably improve the quality of the refractivity retrieval.

Sensor-based clear and cloud radiance calculations in the community radiative transfer model

The community radiative transfer model (CRTM) has been implemented for clear and cloudy satellite radiance simulations in the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation data assimilation system for global and regional forecasting as well as reanalysis for climate studies. Clear-sky satellite radiances are successfully assimilated, while cloudy radiances need to be assimilated for improving precipitation and severe weather forecasting. However, cloud radiance calculations are much slower than the calculations for clear-sky radiance, and exceed our computational capacity for weather forecasting. In order to make cloud radiance assimilation affordable, cloud optical parameters at the band central wavelength are used in the CRTM (OPTRAN-CRTM) where the optical transmittance (OPTRAN) band model is applied. The approximation implies that only one radiative transfer solution for each band (i.e., channel) is needed, instead of typically more than 10,000 solutions that are required for a detailed line-by-line radiative transfer model (LBLRTM). This paper investigated the accuracy of the approximation and helps us to understand the error source. Two NOAA operational sensors, High Resolution Infrared Radiation Sounder/3 (HIRS/3) and Advanced Microwave Sounding Unit (AMSU), have been chosen for this investigation with both clear and cloudy cases. By comparing the CRTM cloud radiance calculations with the LBLRTM simulations, we found that the CRTM cloud radiance model can achieve accuracy better than 0.4 K for the IR sensor and 0.1 K for the microwave sensor. The results suggest that the CRTM cloud radiance calculations may be adequate to the operational satellite radiance assimilation for numerical forecast model. The accuracy using OPTRAN is much better than using the scaling method (SCALING-CRTM). In clear-sky applications, the scaling of the optical depth derived at nadir for brightness temperature calculation at the other direction may result in an error up to about 7 K for some HIRS/3 channels. Under cloudy conditions, SCALING-CRTM may result in an error of about 3.5 K.

Ethiopian Highlands Crop-Climate Prediction: 1979–2009

AbstractThis study compares different methods of predicting crop-related climate in the Ethiopian highlands for the period 1979–2009. A target index (ETH4) is developed as an average of four variables in the June–September season—rainfall, rainfall minus evaporation, estimated latent heat flux, and vegetation, following correlation with crop yields at Melkassa, Ethiopia (8.4°N, 39.3°E, 1550 m elevation). Predictors are drawn from gridded near-global fields of surface temperature, surface air pressure, and 200-hPa zonal wind in the preceding December–March season. Prediction algorithms are formulated by stepwise multivariate regression. The first set of predictors derive from objective principal component (PC) time scores with tropical loading patterns, and the second set is based on key areas determined from correlation with the target index. The second PC of upper zonal wind reveals a tropical–subtropical dipole that is correlated with ETH4 at two-season lead time (correlation coefficient r = −0.53). Point-to-field regression maps show high-latitude signals in surface temperature (positive in North America and negative in Eurasia) and air pressure (negative in the North Pacific Ocean and positive in the South Pacific). Upper zonal winds are most strongly related with ETH4 over the tropical Pacific and Africa at two-season lead time. The multivariate algorithm that is based on PC predictors has an adjusted r2 fit of 0.23, and the algorithm using key-area predictors achieves r2 = 0.37. In comparison, numerical model forecasts reach r2 = 0.33 for ECMWF simulations but are low for other models. The statistical results are specific to the ETH4 index, which is a climate proxy for crop yields in the Ethiopian highlands.

A Proposal for Analyzing and Forecasting Lower-Atmospheric Undular Bores in the Western Gulf of Mexico Region

Abstract A method is presented for analyzing and forecasting the occurrence of lower-atmospheric undular bores over the western and central Gulf of Mexico (GOM) and adjacent land areas using standard operational forecasting and analysis techniques. The method is based on research that has identified a set of sufficient conditions associated with these occurrences realized by observations of the phenomena in recent years and is grounded in the theoretical understanding of undular bores developed during the last century by fluid dynamicists. The approach discusses practical approximations to the theory that allows the operational forecaster to use output from standard numerical and statistical forecast models. In addition to providing an operational method for forecasting the time and location of occurrence, the technique provides a methodology for analyzing and anticipating the strength, forward speed, and horizontal wavelength of the phenomena.

Near real-time estimation of water vapour in the troposphere using ground GNSS and the meteorological data

Abstract. The near real-time (NRT) high resolution water vapour distribution models can be constructed based on GNSS observations delivered from Ground Base Augmentation Systems (GBAS) and ground meteorological data. Since 2008 in the territory of Poland, a GBAS system called ASG-EUPOS (Active Geodetic Network) has been operating. This paper addresses the problems concerning construction of the NRT model of water vapour distribution in the troposphere near Poland. The first section presents all available GNSS and ground meteorological stations in the area of Poland and neighbouring countries. In this section, data feeding scheme is discussed, together with timeline and time resolution. The high consistency between measured and interpolated temperature value is shown, whereas some discrepancy in the pressure is observed. In the second section, the NRT GNSS data processing strategy of ASG-EUPOS network is discussed. Preliminary results show fine alignment of the obtained Zenith Troposphere Delays (ZTDs) with reference data from European Permanent Network (EPN) processing center. The validation of NRT troposphere products against daily solution shows 15 mm standard deviation of obtained ZTD differences. The last section presents the first results of 2-D water vapour distribution above the GNSS network and application of the tomographic model to 3-D distribution of water vapour in the atmosphere. The GNSS tomography model, working on the simulated data from numerical forecast model, shows high consistency with the reference data (by means of standard deviation 4 mm km−1 or 4 ppm), however, noise analysis shows high solution sensitivity to errors in observations. The discrepancy for real data preliminary solution (measured as a mean standard deviation) between reference NWP data and tomography data was on the level of 9 mm km−1 (or 9 ppm) in terms of wet refractivity.

A Comparative Study and Evaluation of Mixing-Height Estimation Based on Sodar-RASS, Ceilometer Data and Numerical Model Simulations

A comparative study and evaluation of mixing-layer height estimation was conducted, using data from remote sensing and in-situ instrumentation, radiosondes, synoptic analyses and model simulations. The data were collected during an experimental campaign conducted at the Athens International Airport, Greece, from 15 to 26 September 2007. Mixing-layer height from the sodar dataset was estimated taking into account the backscatter signal, temperature, Richardson number profiles and surface-based measurements, while for the ceilometer data, the optical attenuated aerosol backscatter intensity first derivative was utilized. Numerical simulations using the Penn State/NCAR MM5 numerical mesoscale model and the Weather Research and Forecast numerical model were also performed. Comparative results under different meteorological conditions (local flows, moderate to strong background flows) are presented and discussed. According to our results under moderate to strong winds the existing mechanical turbulence creates good signal conditions for the two remote systems leading to a good overall agreement between the two methodologies, while both models give reliable estimations of the mixing height. The sodar-RASS system is more suitable under low to moderate winds or when local flows are developed with weak stability, while the ceilometer system is more suitable for moderate to strong winds, which is associated with a homogeneous atmosphere and weaker low-level temperature inversions. In the models, the existing approach for atmospheric boundary-layer depth simulation usually provides higher compared to remote sensing values, especially during local flow events. An alternative approach for the estimation of mixing height by the models, the estimation and use of the diffusion coefficient profiles, is a promising methodology regarding the comparison with the sodar-RASS mixing-height estimations.

Rebound in Atmospheric Predictability and the Role of the Land Surface

Abstract Total predictability within a chaotic system like the earth’s climate cannot increase over time. However, it can be transferred between subsystems. Predictability of air temperature and precipitation in numerical model forecasts over North America rebounds during late spring to summer because of information stored in the land surface. Specifically, soil moisture anomalies can persist over several months, but this memory cannot affect the atmosphere during early spring because of a lack of coupling between land and atmosphere. Coupling becomes established in late spring, enabling the effects of soil moisture anomalies to increase atmospheric predictability in 2-month forecasts begun as early as 1 May. This predictability is maintained through summer and then drops as coupling fades again in fall. This finding suggests summer forecasts of rainfall and air temperature over parts of North America could be significantly improved with soil moisture observations during spring.

Assessing the fidelity of surface currents from a coastal ocean model and HF radar using drifting buoys in the Middle Atlantic Bight

The rapid expansion of urbanization along the world’s coastal areas requires a more comprehensive and accurate understanding of the coastal ocean. Over the past several decades, numerical ocean circulation models have tried to provide such insight, based on our developing understanding of physical ocean processes. The systematic establishment of coastal ocean observation systems adopting cutting-edge technology, such as high frequency (HF) radar, satellite sensing, and gliders, has put such ocean model predictions to the test, by providing comprehensive observational datasets for the validation of numerical model forecasts. The New York Harbor Observing and Prediction System (NYHOPS) is a comprehensive system for understanding coastal ocean processes on the continental shelf waters of New York and New Jersey. To increase confidence in the system’s ocean circulation predictions in that area, a detailed validation exercise was carried out using HF radar and Lagrangian drifter-derived surface currents from three drifters obtained between March and October 2010. During that period, the root mean square (RMS) differences of both the east–west and north–south currents between NYHOPS and HF radar were approximately 15 cm s−1. Harmonic analysis of NYHOPS and HF radar surface currents shows similar tidal ellipse parameters for the dominant M2 tide, with a mean difference of 2.4 cm s−1 in the semi-major axis and 1.4 cm s−1 in the semi-minor axis and 3° in orientation and 10° in phase. Surface currents derived independently from drifters along their trajectories showed that NYHOPS and HF radar yielded similarly accurate results. RMS errors when compared to currents derived along the trajectory of the three drifters were approximately 10 cm s−1. Overall, the analysis suggests that NYHOPS and HF radar had similar skill in estimating the currents over the continental shelf waters of the Middle Atlantic Bight during this time period. An ensemble-based set of particle tracking simulations using one drifter which was tracked for 11 days showed that the ensemble mean separation generally increases with time in a linear fashion. The separation distance is not dominated by high frequency or short spatial scale wavelengths suggesting that both the NYHOPS and HF radar currents are representing tidal and inertial time scales correctly and resolving some of the smaller scale eddies. The growing ensemble mean separation distance is dominated by errors in the mean flow causing the drifters to slowly diverge from their observed positions. The separation distance for both HF radar and NYHOPS stays below 30 km after 5 days, and the two technologies have similar tracking skill at the 95 % level. For comparison, the ensemble mean distance of a drifter from its initial release location (persistence assumption) is estimated to be greater than 70 km in 5 days.