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Pramod Kumar Jangir
,
Kevin C. Ewans
, and
Ian R. Young

Abstract

Accurate measurements of ocean waves underpin efficient offshore operations and optimal offshore structure design, helping to ensure the offshore industry can operate both safely and economically. Popular instruments used by the offshore industry are the Rosemount WaveRadar (Radar) and the Waverider Buoy. The Optech Laser has been used at some locations for specific studies. Recent reports indicate systematic differences of order 10% among the wave measurements made by these instruments. This paper examines the relative performance of these instruments based upon various time-domain comparisons, including results from a quality control (QC) procedure, capabilities of measuring the wave surface profile (skewness), and crest heights for varying wind sea and swell conditions. The QC check provides good-quality data that can be further investigated with an assurance of error-free data, suggesting that the Waverider produces the best-quality data with the lowest failure rate compared to the Laser and Radar. A significant number of the Waverider surface elevation records have negative skewness, particularly at higher sea states, affecting its crest height measurements, which are lower than those from the Laser and Radar. Additionally, the significant wave height (H 1/3) estimates of the Radar are lower than the Laser and Waverider, but its zero-crossing wave periods (TZ ), on average, are longer than the Laser and the Waverider. The significant heights (H 1/3) of Laser and Waverider are in good agreement for all three datasets, but the Waverider’s zero-crossing wave period (TZ ) estimates are significantly longer than the Laser.

Restricted access
Anzhou Cao
,
Zheng Guo
,
Shuya Wang
,
Xinyu Guo
, and
Jinbao Song

Abstract

With the development of ocean observation technology, data from specially designed mobile profiling floats have been used to study the internal tides (ITs). However, the accuracy of IT characteristics extracted from such observations has not been fully evaluated. Based on numerical simulations of ITs and background circulation with hundreds of free-moving floats near the Luzon Strait, this study examines the IT characteristics extracted from the float observations based on statistics. For the case in which only the M2 constituent is considered, the lowest error level of extracted M2 temperature fluctuation amplitudes (TFAs) is 40%–50%, which appears at 200–1500 m depth. Increasing the sampling frequency of the float from daily to hourly does not decrease the lowest error level. The quasi-daily sampling and other tidal constituents also have an impact on the extracted M2 TFAs and increase their errors. The different patterns of background currents mainly influence the errors of extracted M2 TFAs in the upper 200 m. The relation between TFA and vertical displacement of ITs and the two error sources of the TFA extracted from float observations are discussed in this study.

Restricted access
Connor Pearson
,
Tian-You Yu
,
David Bodine
,
Sebastian Torres
, and
Anthony Reinhart

Abstract

Downbursts are rapidly evolving meteorological phenomena with numerous vertically oriented precursor signatures, and the temporal resolution and vertical sampling of the current NEXRAD system are too coarse to observe their evolution and precursor signatures properly. A future all-digital polarimetric phased-array weather radar (PAR) should be able to improve both temporal resolution and spatial sampling of the atmosphere to provide better observations of rapidly evolving hazards such as downbursts. Previous work has been focused on understanding the trade-offs associated with using various scanning techniques on stationary PARs; however, a rotating, polarimetric PAR (RPAR) is a more feasible and cost-effective candidate. Thus, understanding the trade-offs associated with using various scanning techniques on an RPAR is vital in learning how to best observe downbursts with such a system. This work develops a framework for analyzing the trade-offs associated with different scanning strategies in the observation of downbursts and their precursor signatures. A proof-of-concept analysis—which uses a Cloud Model 1 (CM1)-simulated downburst-producing thunderstorm—is also performed with both conventional and imaging scanning strategies in an adaptive scanning framework to show the potential value and feasibility of the framework. Preliminary results from the proof-of-concept analysis indicate that there is indeed a limit to the benefits of imaging as an update time speedup method. As imaging is used to achieve larger speedup factors, corresponding data degradation begins to hinder the observations of various precursor signatures.

Open access
Ziying Yang
,
Jiping Liu
,
Chao-Yuan Yang
, and
Yongyun Hu

Abstract

Sea surface temperature (SST) forecast products from the NCEP Climate Forecast System (CFSv2) that are widely used in climate research and prediction have nonstationary bias. In this study, we develop single- (ANN1) and three-hidden-layer (ANN3) neural networks and examine their ability to correct the SST bias in the NCEP CFSv2 extended seasonal forecast starting from July in the extratropical Northern Hemisphere. Our results show that the ensemble-based ANN1 and ANN3 can reduce the uncertainty associated with parameters assigned initially and dependence on random sampling. Overall, ANN1 reduces RMSE of the CFSv2 forecast SST substantially by 0.35°C (0.34°C) for the testing (training) data and ANN3 further reduces RMSE relatively by 0.49°C (0.47°C). Both the ensemble-based ANN1 and ANN3 can significantly reduce the spatially and temporally varying bias of the CFSv2 forecast SST in the Pacific and Atlantic Oceans, and ANN3 shows better agreement with the observation than that of ANN1 in some subregions.

Significance Statement

Global coupled climate models are the primary tool for climate simulation and prediction and provide initial and boundary conditions to drive regional climate models. SST is an essential climate variable simulated and forecast by global climate models, which suffers substantial biases both spatially and temporally. We apply the ensemble averaging of both single- and three-hidden-layer neural networks on the NCEP CFSv2 SST forecast. They can correct the identified SST error, though ANN3 performs relatively better than that of ANN1. Thus, ensemble-based ANN3 is a useful SST bias correction approach.

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N. C. Privé
,
Matthew McLinden
,
Bing Lin
,
Isaac Moradi
,
Meta Sienkiewicz
,
G. M. Heymsfield
, and
Will McCarty

Abstract

A new instrument has been proposed for measuring surface air pressure over the marine surface with a combined active/passive scanning multichannel differential absorption radar to provide an estimate of the total atmospheric column oxygen content. A demonstrator instrument, the Microwave Barometric Radar and Sounder (MBARS), has been funded by the National Aeronautics and Space Administration for airborne test missions. Here, a proof-of-concept study to evaluate the potential impact of spaceborne surface pressure data on numerical weather prediction is performed using the Goddard Modeling and Assimilation Office global observing system simulation experiment (OSSE) framework. This OSSE framework employs the Goddard Earth Observing System model and the hybrid 4D ensemble variational Gridpoint Statistical Interpolation data assimilation system. Multiple flight and scanning configurations of potential spaceborne orbits are examined. Swath width and observation spacing for the surface pressure data are varied to explore a range of sampling strategies. For wider swaths, the addition of surface pressures reduces the root-mean-square surface pressure analysis error by as much as 20% over some ocean regions. The forecast sensitivity observation impact tool estimates impacts on the Pacific Ocean basin boundary layer 24-h forecast temperatures for spaceborne surface pressures that are on par with rawinsondes and aircraft and estimates greater impacts than the current network of ships and buoys. The largest forecast impacts are found in the Southern Hemisphere extratropics.

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S. L. Durden
,
R. M. Beauchamp
,
S. Graniello
,
V. Venkatesh
, and
S. Tanelli

Abstract

The displaced phased center antenna (DPCA) method of clutter cancellation for ground moving target detection from airborne platforms has been in use for a number of decades. Application of the DPCA method for spaceborne Doppler weather radar velocity estimation was suggested in 2007. The initial description and analysis of the technique was followed several years ago by demonstration using a multiantenna airborne radar. Recent reviews of methods and technology for spaceborne cloud and precipitation radar have also mentioned possible use of DPCA. However, to date, analyses of the application of DPCA to spaceborne Doppler weather radar have assumed that the two channels and antennas are identical, including perfect alignment, and that the DPCA condition is well-satisfied. This study uses simulation to examine the effects of relaxing these assumptions. The simulation method and its validation are discussed, with companion analytical calculations in the appendix. Next, simulations are used to show the effects on the Doppler estimates from errors in pointing and positioning relative to the ideal DPCA. The DPCA technique is relatively robust to possible errors, indicating that a practical DPCA radar system can provide precise Doppler measurements from space.

Significance Statement

Analytical and simulation results show that the displaced phase center antenna approach can enable spaceborne atmospheric Doppler radar measurements with good accuracy, even in the presence of antenna mispointing and other system errors.

Restricted access
Jie Yu
,
Cheryl Ann Blain
,
Paul J. Martin
, and
Tim J. Campbell

Abstract

Presented is the approach, implementation, and evaluation of two-way nesting in a split-implicit ocean model, the Navy Coastal Ocean Model (NCOM). Emphasis is on the strategies applied to feed back fields from the fine-mesh nest (child grid) to the coarse mesh (parent grid). On an appropriate separation of dynamic and feedback interfaces, attention is especially needed for the feedback interface of surface elevation. One particular issue addressed is the inconsistency between the 3D baroclinic velocities and 2D barotropic transports in the feedback. The discrepancy is inherently associated with bathymetry, depth integration, and the need to average over spatial grid points. A simple remedy is proposed and proven to be effective and necessary in realistic coastal applications. In addition to the full two-way nesting, a simplified two-way nesting approach is provided in which only the temperature and salinity are fed back from the nest, and the velocity fields are assumed to self-adjust according to the geostrophic balance. The performance of both approaches is evaluated using the idealized benchmark, propagation of a baroclinic vortex, and an application to the Mississippi River outflow in the northeast Gulf of Mexico, including a comparison with available observations. Discussions are also made on the computational efficiency of the two-way nesting and its sensitivity to the open boundary conditions in regard to noise suppression.

Significance Statement

The two-way nesting approach reported here can be adapted to other structured-grid ocean models, in particular those using the split-implicit technique. The treatment of the feedback interface for surface elevation is especially important for suppressing the noise production and improving the feedback consistency. An effective procedure is given to amend the inconsistency in the velocity field feedback that is inherently due to bathymetry.

Restricted access
Douglas Vandemark
,
Marc Emond
,
Scott D. Miller
,
Shawn Shellito
,
Ivan Bogoev
, and
Jason M. Covert

Abstract

One long-standing technical problem affecting the accuracy of eddy correlation air–sea CO2 flux estimates has been motion contamination of the CO2 mixing-ratio measurement. This sensor-related problem is well known but its source remains unresolved. This report details an attempt to identify and reduce motion-induced error and to improve the infrared gas analyzer (IRGA) design. The key finding is that a large fraction of the motion sensitivity is associated with the detection approach common to most closed- and open-path IRGA employed today for CO2 and H2O measurements. A new prototype sensor was developed to both investigate and remedy the issue. Results in laboratory and deep-water tank tests show marked improvement. The prototype shows a factor of 4–10 reduction in CO2 error under typical at-sea buoy pitch and roll tilts in comparison with an off-the-shelf IRGA system. A similar noise reduction factor of 2–8 is observed in water vapor measurements. The range of platform tilt motion testing also helps to document motion-induced error characteristics of standard analyzers. Study implications are discussed including findings relevant to past field measurements and the promise for improved future flux measurements using similarly modified IRGA on moving ocean observing and aircraft platforms.

Open access
Valentin Louf
and
Alain Protat

Abstract

We present an integrated framework that leverages multiple weather radar calibration and monitoring techniques to provide real-time diagnostics on reflectivity calibration, antenna pointing, and dual-polarization moments. This framework uses a volume-matching technique to track the absolute calibration of radar reflectivity with respect to the Global Precipitation Measurement (GPM) spaceborne radar, the relative calibration adjustment (RCA) technique to track relative changes in the radar calibration constant, the solar calibration technique to track daily change in solar power and antenna pointing error, and techniques that track properties of light-rain medium to monitor the differential reflectivity and dual-polarization moments. This framework allows for an evaluation of various calibration and monitoring techniques. For example, we found that a change in the RCA is highly correlated to a change in absolute calibration, with respect to GPM, if a change in antenna pointing can first be ruled out. It is currently monitoring 67+ radars from the Australian radar network. Because of the diverse and evolving nature of the Australian radar network, flexibility and modularity are at the core of the calibration framework. The framework can tailor its diagnostics to the specific characteristics of a radar (band, beamwidth, etc.). Because of its modularity, it can be expanded with new techniques to provide additional diagnostics (e.g., monitoring of radar sensitivity). The results are presented in an interactive dashboard at different level of details for a wide and diverse audience (radar engineers, researchers, forecasters, and management), and it is operational at the Australian Bureau of Meteorology.

Significance Statement

Weather radars, like all instruments, require maintenance and upgrades. Rainfall measurements are highly variable and sensitive to change, and this can lead to inconsistencies within a radar network. Calibration is the process to counteract those inconsistencies. Any calibration requires a fixed standard to which the changed/upgraded radar can be compared. The SCAR calibration framework presented herein makes use of several standards to retrieve a full set of diagnostics about the radar data. We apply these techniques over the entire Australian weather radar network and demonstrate that, by using this integrated approach, absolute calibration can be achieved to within 1 dBZ of reflectivity, antenna pointing can be monitored within 0.1°, and the various measurements of the radars can be quality controlled.

Restricted access
Philippe Baron
,
Kohei Kawashima
,
Dong-Kyun Kim
,
Hiroshi Hanado
,
Seiji Kawamura
,
Takeshi Maesaka
,
Katsuhiro Nakagawa
,
Shinsuke Satoh
, and
Tomoo Ushio

Abstract

We present nowcasts of sudden heavy rains on meso-γ scales (2–20 km) using the high spatiotemporal resolution of a multiparameter phased-array weather radar (MP-PAWR) sensitive to rain droplets. The onset of typical storms is successfully predicted with 10-min lead time, i.e., the current predictability limit of rainfall caused by individual convective cores. A supervised recurrent neural network based on long short-term memory with 3D spatial convolutions (RN3D) is used to account for the horizontal and vertical changes of the convective cells with a time resolution of 30 s. The model uses radar reflectivity at horizontal polarization ZH and the differential reflectivity. The input parameters are defined in a volume of 64 × 64 × 8 km3 with the lowest level at 1.9 km and a resolution of 0.4 × 0.4 × 0.25 km3. The prediction is a 10-min sequence of ZH at the lowest grid level. The model is trained with a large number of observations of summer 2020 and an adversarial technique. RN3D is tested with different types of rapidly evolving localized heavy rainfalls of summers 2018 and 2019. The model performance is compared to that of an advection model for 3D extrapolation of PAWR echoes (A3DM). RN3D better predicts the formation and dissipation of precipitation. However, RN3D tends to underestimate heavy rainfall especially when the storm is well developed. In this phase of the storm, A3DM nowcast scores are found slightly higher. The high skill of RN3D to predict the onset of sudden localized rainfall is illustrated with an example for which RN3D outperforms the operational precipitation nowcasting system of Japan Meteorological Agency (JMA).

Significance Statement

Temporal extrapolation of radar observations is a means of nowcasting sudden heavy rains, i.e., forecasts with a few tens of minutes and a high spatial resolution better than 500 m. They are necessary to set up warning systems to anticipate damage to infrastructure and reduce the fatalities these storms cause. It is a difficult task due to the storm suddenness, restricted area, and nonlinear behavior that are not well captured by current operational observation and numerical systems. In this study, we use a new high-resolution weather radar with polarimetric information and a 3D recurrent neural network to improve 10-min nowcasts, the current limit of operational systems. This is a first and essential step before applying such a method for increasing the prediction lead time.

Open access