Comparison of the third-generation Japanese ocean flux data set J-OFURO3 with numerical simulations of Typhoon Dujuan (2015) traveling south of Okinawa

Abstract Insufficient in situ observations in high winds make it difficult to verify climatological data sets and the results of tropical cyclone (TC) simulations. Reliable data sets are necessary for developing numerical models that predict TCs more accurately. This study attempted to compare the third-generation Japanese Ocean Flux Data Sets with Use of Remote-Sensing Observations (J-OFURO3) data, with TC simulations conducted by a 2 km mesh coupled atmosphere-wave-ocean model. This is a case study of Typhoon Dujuan (2015) and the area of approximately 20̊N, 130̊E, south of Okinawa, was selected. The comparison reveals that J-OFURO3 data are reliable for verifying the atmospheric and oceanic components of TC simulations with two different initial sea surface temperature (SST) conditions, although the blank area remains within the inner core area for air temperature, specific humidity, and latent heat flux owing to issues with the construction method. Simulated maximum surface wind speeds (MSWs) are significantly correlated with J-OFURO3 MSWs. The asymmetrical distribution of simulated surface wind speeds within the inner core area can be reproduced well in the J-OFURO3 data set. In terms of the oceanic response to the TC, TC-induced sea surface cooling was reproduced well in the J-OFURO3 data set and is consistent with the simulation results. Unlike simulated SST, simulated surface wind speeds, surface air temperature, and surface specific humidity are still inconsistent with the J-OFURO3 data, even when the J-OFURO3 SST is used as the initial condition. New algorithms, more satellite data used, and model improvement are expected in the future.

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The Probability Distribution of Sea Surface Wind Speeds: Effects of Variable Surface Stratification and Boundary Layer Thickness

Journal of Climate ◽

10.1175/2010jcli3184.1 ◽

2010 ◽

Vol 23 (19) ◽

pp. 5151-5162 ◽

Cited By ~ 6

Author(s):

Adam Hugh Monahan

Keyword(s):

Boundary Layer ◽

Layer Thickness ◽

Boundary Layer Thickness ◽

Surface Wind ◽

Sea Surface ◽

Surface Winds ◽

Free Atmosphere ◽

Wind Speeds ◽

Sea Surface Wind ◽

Variable Surface

Abstract Air–sea exchanges of momentum, energy, and material substances of fundamental importance to the variability of the climate system are mediated by the character of the turbulence in the atmospheric and oceanic boundary layers. Sea surface winds influence, and are influenced by, these fluxes. The probability density function (pdf) of sea surface wind speeds p(w) is a mathematical object describing the variability of surface winds that arises from the physics of the turbulent atmospheric planetary boundary layer. Previous mechanistic models of the pdf of sea surface wind speeds have considered the momentum budget of an atmospheric layer of fixed thickness and neutral stratification. The present study extends this analysis, using an idealized model to consider the influence of boundary layer thickness variations and nonneutral surface stratification on p(w). It is found that surface stratification has little direct influence on p(w), while variations in boundary layer thickness bring the predictions of the model into closer agreement with the observations. Boundary layer thickness variability influences the shape of p(w) in two ways: through episodic downward mixing of momentum into the boundary layer from the free atmosphere and through modulation of the importance (relative to other tendencies) of turbulent momentum fluxes at the surface and the boundary layer top. It is shown that the second of these influences dominates over the first.

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Prediction of Progestin Affinity for the Human Progesterone Receptor Based on Corrected RBA Data

Biomedical Chemistry Research and Methods ◽

10.18097/bmcrm00080 ◽

2018 ◽

Vol 1 (4) ◽

pp. e00080

Author(s):

A.V. Mikurova ◽

V.S. Skvortsov

Keyword(s):

Progesterone Receptor ◽

Prediction Equation ◽

Binding Activity ◽

Data Sets ◽

Data Set ◽

The Third ◽

Relative Binding ◽

Human Progesterone Receptor ◽

Nuclear Progesterone Receptor

The modeling of complexes of 3 sets of steroid and nonsteroidal progestins with the ligand-binding domain of the nuclear progesterone receptor was performed. Molecular docking procedure, long-term simulation of molecular dynamics and subsequent analysis by MM-PBSA (MM-GBSA) were used to model the complexes. Using the characteristics obtained by the MM-PBSA method two data sets of steroid compounds obtained in different scientific groups a prediction equation for the value of relative binding activity (RBA) was constructed. The RBA value was adjusted so that in all samples the actual activity was compared with the progesterone activity. The third data set of nonsteroidal compounds was used as a test. The resulted equation showed that the prediction results could be applied to both steroid molecules and nonsteroidal progestins.

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Data Sets

Hurricane Climatology ◽

10.1093/oso/9780199827633.003.0009 ◽

2013 ◽

Author(s):

James B. Elsner ◽

Thomas H. Jagger

Keyword(s):

Model Building ◽

Careful Analysis ◽

Logical Structure ◽

Fortran Program ◽

Data Sets ◽

Data Set ◽

Near Surface ◽

Wind Speeds ◽

The North ◽

The One

Hurricane data originate from careful analysis of past storms by operational meteorologists. The data include estimates of the hurricane position and intensity at 6-hourly intervals. Information related to landfall time, local wind speeds, damages, and deaths, as well as cyclone size, are included. The data are archived by season. Some effort is needed to make the data useful for hurricane climate studies. In this chapter, we describe the data sets used throughout this book. We show you a work flow that includes importing, interpolating, smoothing, and adding attributes. We also show you how to create subsets of the data. Code in this chapter is more complicated and it can take longer to run. You can skip this material on first reading and continue with model building in Chapter 7. You can return here when you have an updated version of the data that includes the most recent years. Most statistical models in this book use the best-track data. Here we describe these data and provide original source material. We also explain how to smooth and interpolate them. Interpolations are needed for regional hurricane analyses. The best-track data set contains the 6-hourly center locations and intensities of all known tropical cyclones across the North Atlantic basin, including the Gulf of Mexico and Caribbean Sea. The data set is called HURDAT for HURricane DATa. It is maintained by the U.S. National Oceanic and Atmospheric Administration (NOAA) at the National Hurricane Center (NHC). Center locations are given in geographic coordinates (in tenths of degrees) and the intensities, representing the one-minute near-surface (∼10 m) wind speeds, are given in knots (1 kt = .5144 m s−1) and the minimum central pressures are given in millibars (1 mb = 1 hPa). The data are provided in 6-hourly intervals starting at 00 UTC (Universal Time Coordinate). The version of HURDAT file used here contains cyclones over the period 1851 through 2010 inclusive. Information on the history and origin of these data is found in Jarvinen et al (1984). The file has a logical structure that makes it easy to read with a FORTRAN program. Each cyclone contains a header record, a series of data records, and a trailer record.

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A Wind Speed Retrieval Model for Sentinel-1A EW Mode Cross-Polarization Images

Remote Sensing ◽

10.3390/rs11020153 ◽

2019 ◽

Vol 11 (2) ◽

pp. 153 ◽

Cited By ~ 3

Author(s):

Yuan Gao ◽

Changlong Guan ◽

Jian Sun ◽

Lian Xie

Keyword(s):

Wind Speed ◽

Incidence Angle ◽

Surface Wind ◽

European Space Agency ◽

Sea Surface ◽

Cross Polarization ◽

Retrieval Model ◽

Sar Images ◽

Wind Speeds ◽

Sea Surface Wind

In contrast to co-polarization (VV or HH) synthetic aperture radar (SAR) images, cross-polarization (CP for VH or HV) SAR images can be used to retrieve sea surface wind speeds larger than 20 m/s without knowing the wind directions. In this paper, a new wind speed retrieval model is proposed for European Space Agency (ESA) Sentinel-1A (S-1A) Extra-Wide swath (EW) mode VH-polarized images. Nineteen S-1A images under tropical cyclone condition observed in the 2016 hurricane season and the matching data from the Soil Moisture Active Passive (SMAP) radiometer are collected and divided into two datasets. The relationships between normalized radar cross-section (NRCS), sea surface wind speed, wind direction and radar incidence angle are analyzed for each sub-band, and an empirical retrieval model is presented. To correct the large biases at the center and at the boundaries of each sub-band, a corrected model with an incidence angle factor is proposed. The new model is validated by comparing the wind speeds retrieved from S-1A images with the wind speeds measured by SMAP. The results suggest that the proposed model can be used to retrieve wind speeds up to 35 m/s for sub-bands 1 to 4 and 25 m/s for sub-band 5.

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Novel Approaches to Smoothing and Comparing SELDI TOF Spectra

Cancer Informatics ◽

10.1177/117693510500100109 ◽

2005 ◽

Vol 1 ◽

pp. 117693510500100 ◽

Cited By ~ 4

Author(s):

Sreelatha Meleth ◽

Isam-Eldin Eltoum ◽

Liu Zhu ◽

Denise Oelschlager ◽

Chandrika Piyathilake ◽

...

Keyword(s):

Spectral Analysis ◽

Fourier Transforms ◽

Area Under The Curve ◽

Maximum Intensity ◽

Data Sets ◽

Intensity Level ◽

Prominent Feature ◽

Data Set ◽

The Third ◽

Novel Approaches

Background Most published literature using SELDI-TOF has used traditional techniques in Spectral Analysis such as Fourier transforms and wavelets for denoising. Most of these publications also compare spectra using their most prominent feature, ie, peaks or local maximums. Methods The maximum intensity value within each window of differentiable m/z values was used to represent the intensity level in that window. We also calculated the ‘Area under the Curve’ (AUC) spanned by each window. Results Keeping everything else constant, such as pre-processing of the data and the classifier used, the AUC performed much better as a metric of comparison than the peaks in two out of three data sets. In the third data set both metrics performed equivalently. Conclusions This study shows that the feature used to compare spectra can have an impact on the results of a study attempting to identify biomarkers using SELDI TOF data.

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Estimating observation and model error variances using multiple data sets

Atmospheric Measurement Techniques ◽

10.5194/amt-11-4239-2018 ◽

2018 ◽

Vol 11 (7) ◽

pp. 4239-4260 ◽

Cited By ~ 8

Author(s):

Richard Anthes ◽

Therese Rieckh

Keyword(s):

Error Variance ◽

Specific Humidity ◽

Data Sets ◽

Data Set ◽

Multiple Data ◽

Gfs Model ◽

Multiple Data Sets ◽

Using Data ◽

The Tropics ◽

Estimated Error

Abstract. In this paper we show how multiple data sets, including observations and models, can be combined using the “three-cornered hat” (3CH) method to estimate vertical profiles of the errors of each system. Using data from 2007, we estimate the error variances of radio occultation (RO), radiosondes, ERA-Interim, and Global Forecast System (GFS) model data sets at four radiosonde locations in the tropics and subtropics. A key assumption is the neglect of error covariances among the different data sets, and we examine the consequences of this assumption on the resulting error estimates. Our results show that different combinations of the four data sets yield similar relative and specific humidity, temperature, and refractivity error variance profiles at the four stations, and these estimates are consistent with previous estimates where available. These results thus indicate that the correlations of the errors among all data sets are small and the 3CH method yields realistic error variance profiles. The estimated error variances of the ERA-Interim data set are smallest, a reasonable result considering the excellent model and data assimilation system and assimilation of high-quality observations. For the four locations studied, RO has smaller error variances than radiosondes, in agreement with previous studies. Part of the larger error variance of the radiosondes is associated with representativeness differences because radiosondes are point measurements, while the other data sets represent horizontal averages over scales of ∼ 100 km.

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Fennec dust forecast intercomparison over the Sahara in June 2011

Atmospheric Chemistry and Physics ◽

10.5194/acp-16-6977-2016 ◽

2016 ◽

Vol 16 (11) ◽

pp. 6977-6995 ◽

Cited By ~ 16

Author(s):

Jean-Pierre Chaboureau ◽

Cyrille Flamant ◽

Thibaut Dauhut ◽

Cécile Kocha ◽

Jean-Philippe Lafore ◽

...

Keyword(s):

High Resolution ◽

Surface Wind ◽

Dust Emission ◽

Airborne Lidar ◽

Data Sets ◽

Density Currents ◽

Data Set ◽

Near Surface ◽

Western Sahara ◽

Deep Well

Abstract. In the framework of the Fennec international programme, a field campaign was conducted in June 2011 over the western Sahara. It led to the first observational data set ever obtained that documents the dynamics, thermodynamics and composition of the Saharan atmospheric boundary layer (SABL) under the influence of the heat low. In support to the aircraft operation, four dust forecasts were run daily at low and high resolutions with convection-parameterizing and convection-permitting models, respectively. The unique airborne and ground-based data sets allowed the first ever intercomparison of dust forecasts over the western Sahara. At monthly scale, large aerosol optical depths (AODs) were forecast over the Sahara, a feature observed by satellite retrievals but with different magnitudes. The AOD intensity was correctly predicted by the high-resolution models, while it was underestimated by the low-resolution models. This was partly because of the generation of strong near-surface wind associated with thunderstorm-related density currents that could only be reproduced by models representing convection explicitly. Such models yield emissions mainly in the afternoon that dominate the total emission over the western fringes of the Adrar des Iforas and the Aïr Mountains in the high-resolution forecasts. Over the western Sahara, where the harmattan contributes up to 80 % of dust emission, all the models were successful in forecasting the deep well-mixed SABL. Some of them, however, missed the large near-surface dust concentration generated by density currents and low-level winds. This feature, observed repeatedly by the airborne lidar, was partly forecast by one high-resolution model only.

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Application of state-of-the-art SAR X-band geophysical model functions (GMFs) for sea surface wind (SSW) speed retrieval to a data set of the Italian satellite mission COSMO-SkyMed

International Journal of Remote Sensing ◽

10.1080/01431161.2015.1034893 ◽

2015 ◽

Vol 36 (9) ◽

pp. 2296-2312 ◽

Cited By ~ 5

Author(s):

G. Grieco ◽

F. Nirchio ◽

M. Migliaccio

Keyword(s):

State Of The Art ◽

Surface Wind ◽

Sea Surface ◽

Satellite Mission ◽

Data Set ◽

Sea Surface Wind ◽

Geophysical Model ◽

X Band

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Sea Surface Wind Correction Using HF Ocean Radar and Its Impact on Coastal Wave Prediction

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-16-0249.1 ◽

2017 ◽

Vol 34 (9) ◽

pp. 2001-2020 ◽

Cited By ~ 3

Author(s):

Yukiharu Hisaki

Keyword(s):

Wind Direction ◽

Surface Wind ◽

Hf Radar ◽

Sea Surface ◽

Wave Parameter ◽

Simple Method ◽

Wind Speeds ◽

Wave Prediction ◽

Sea Surface Wind ◽

The Impact

AbstractBoth wind speeds and wind directions are important for predicting wave heights near complex coastal areas, such as small islands, because the fetch is sensitive to the wind direction. High-frequency (HF) radar can be used to estimate sea surface wind directions from first-order scattering. A simple method is proposed to correct sea surface wind vectors from reanalysis data using the wind directions estimated from HF radar. The constraints for wind speed corrections are that the corrections are small and that the corrections of horizontal divergences are small. A simple algorithm for solving the solution that minimizes the weighted sum of the constraints is developed. Another simple method is proposed to correct sea surface wind vectors. The constraints of the method are that corrections of wind vectors and horizontal divergences from the reanalysis wind vectors are small and that the projection of the corrected wind vectors to the direction orthogonal to the HF radar–estimated wind direction is small. The impact of wind correction on wave parameter prediction is large in the area in which the fetch is sensitive to wind direction. The accuracy of the wave prediction is improved by correcting the wind in that area, where correction of wind direction is more important than correction of wind speeds for the improvement. This method could be used for near-real-time wave monitoring by correcting forecast winds using HF radar data.

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