Typhoon Warm-Core Structures Derived from FY-3D MWTS-2 Observations

In this study, the three-dimensional (3D) warm-core structures of the Northwest Pacific typhoons Francisco, Lekima, and Krosa in August 2019 are retrieved from the Fengyun-3D (FY-3D) microwave temperature sounder-2 (MWTS-2) observations of brightness temperature. Due to the lack of two window channels at 23.8 GHz and 31.4 GHz, an empirical cloud detection algorithm based on 50.3 GHz bias-corrected observations-minus-backgrounds is applied to obtain clear-sky observations for the multiple linear regression retrieval algorithm. The MWTS-2 cloud-affected channels 3–5 are not used to retrieve temperatures under cloudy conditions to eliminate low-tropospheric cold anomalies. The multiple linear regression coefficients are obtained based on MWTS-2 brightness temperatures and the temperatures from the European Centre for Medium-Range Weather Forecasts Reanalysis-5 (ERA5) in the training period of three weeks before the month of targeted typhoons. The proposed MWTS-2 warm-core retrieval can well capture the radial and vertical temporal evolutions of the temperature anomalies of the typhoons Francisco, Lekima, and Krosa. The sizes of the warm-core anomalies of typhoons Lekima and Krosa retrieved by the MWTS-2 are horizontally and vertically similar to and stronger than those of the ERA5. Compared with the ERA5 reanalysis in August 2019, the biases for MWTS-2 temperature retrievals are smaller than ±0.25 K, with root-mean-square errors (RMSEs) smaller than and 2.0 K at all altitudes. Additionally, the location of the 250-hPa maximum temperature anomaly retrieved by the MWTS-2 is closer to the best track than that of the ERA5. A weak warm-core around 200 hPa and a cold-core anomaly in the middle troposphere are also found in the outer rain bands region due to the effect of evaporation of rainfall.

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Applicability of Multiple Linear Regression Analysis to the Analysis of Three-dimensional Changes in Perioral Soft Tissue Following Mandibular Setback Surgery

The Japanese Journal of Jaw Deformities ◽

10.5927/jjjd.22.244 ◽

2012 ◽

Vol 22 (4) ◽

pp. 244-254 ◽

Cited By ~ 1

Author(s):

YURI YAKITA ◽

KAZUTO TERADA ◽

AKIHIRO KOHARA ◽

KANAKO OCHI ◽

RITSUO TAKAGI ◽

...

Keyword(s):

Regression Analysis ◽

Soft Tissue ◽

Linear Regression ◽

Multiple Linear Regression ◽

Linear Regression Analysis ◽

Three Dimensional ◽

Multiple Linear Regression Analysis ◽

Dimensional Changes ◽

Mandibular Setback ◽

Mandibular Setback Surgery

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A new algorithm for detecting cloud height using OMPS/LP measurements

Atmospheric Measurement Techniques ◽

10.5194/amt-9-1239-2016 ◽

2016 ◽

Vol 9 (3) ◽

pp. 1239-1246 ◽

Cited By ~ 9

Author(s):

Zhong Chen ◽

Matthew DeLand ◽

Pawan K. Bhartia

Keyword(s):

Lower Boundary ◽

Vertical Gradient ◽

Satellite Observation ◽

Detection Algorithm ◽

Retrieval Algorithm ◽

Cloud Detection ◽

Near Ir ◽

Cloud Height ◽

Good Agreement

Abstract. The Ozone Mapping and Profiler Suite Limb Profiler (OMPS/LP) ozone product requires the determination of cloud height for each event to establish the lower boundary of the profile for the retrieval algorithm. We have created a revised cloud detection algorithm for LP measurements that uses the spectral dependence of the vertical gradient in radiance between two wavelengths in the visible and near-IR spectral regions. This approach provides better discrimination between clouds and aerosols than results obtained using a single wavelength. Observed LP cloud height values show good agreement with coincident Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) measurements.

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Warm Core Structure of Hurricane Erin Diagnosed from High Altitude Dropsondes during CAMEX-4

Journal of the Atmospheric Sciences ◽

10.1175/jas3596.1 ◽

2006 ◽

Vol 63 (1) ◽

pp. 309-324 ◽

Cited By ~ 78

Author(s):

J. B. Halverson ◽

J. Simpson ◽

G. Heymsfield ◽

H. Pierce ◽

T. Hock ◽

...

Keyword(s):

Temperature Anomaly ◽

Numerical Models ◽

Three Dimensional ◽

Maximum Temperature ◽

Airborne Radar ◽

The Core ◽

Warm Core ◽

Satellite Sensors ◽

Consistent Picture ◽

Small Shear

Abstract A combination of multiaircraft and several satellite sensors were used to examine the core of Hurricane Erin on 10 September 2001, as part of the Fourth Convection and Moisture Experiment (CAMEX-4) program. During the first set of aircraft passes, around 1700 UTC, Erin was still at its maximum intensity with a central pressure of 969 hPa and wind speed of 105 kt (54 m s−1). The storm was moving slowly northwestward at 4 m s−1, over an increasingly colder sea surface. Three instrumented aircraft, the National Oceanic and Atmospheric Administration (NOAA) P3 with radar, the National Aeronautics and Space Administration (NASA) ER-2 at 19 km, newly equipped with GPS dropwindsondes, and the NASA DC-8 with dropwindsondes flew in formation across the eye at about 1700 UTC and again 2.5 h later around 1930 UTC. The storm had weakened by 13 m s−1 between the first and second eye penetrations. The warm core had a maximum temperature anomaly of only 11°C, located at 500 hPa, much weaker and lower than active hurricanes. The core appeared to slant rearward above 400 hPa. Even on the first penetration, airborne radar showed that the eyewall cloud towers were dying. The tops fell short of reaching 15 km and a melting band was found throughout. The tropopause had a bulge to 15.8-km elevation (environment ∼14.4 km) above the dying convection. The paper presents a consistent picture of the vortex in shear interaction from a primarily thermodynamic perspective. A feature of Erin at this time was a pronounced wavenumber-1 convective asymmetry with all convective activity being confined to the forward quadrants on the left side of the shear vector as calculated from analyses. This is similar to that predicted by the mesoscale numerical models, which also predict that such small amounts of shear would not affect the storm intensity. In Erin, it is remarkable that relatively small shear produced such a pronounced asymmetry in the convection. From the three-dimensional analysis of dropsonde data, horizontal asymmetries in lower and middle tropospheric warming were identified. The warm anomalies are consistent with the pattern of mesoscale vertical motions inferred from the shear-induced wavenumber-1 asymmetry, dipole in rain intensity, and surface convergence.

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Effect of Decompression on Jaw Cystic Lesions Based on Three-Dimensional Volumetric Analysis

Medicina ◽

10.3390/medicina56110602 ◽

2020 ◽

Vol 56 (11) ◽

pp. 602

Author(s):

Yeh-Jin Kwon ◽

Kyeong-Soo Ko ◽

Byung-Kyu So ◽

Dong-Hyuck Kim ◽

Hyon-Seok Jang ◽

...

Keyword(s):

Computed Tomography ◽

Linear Regression ◽

Multiple Linear Regression ◽

Young Patient ◽

Mixed Models ◽

Three Dimensional ◽

Volumetric Analysis ◽

Patient Age ◽

Patient Âgé ◽

Posterior Maxilla

Background and objectives: This study aimed to evaluate the effectiveness of decompression on jaw cysts according to various parameters by volumetric analysis using three-dimensional computed tomography. Materials and methods: Fifty patients who underwent surgical decompression of the jaw cystic lesion were selected, and their preoperative and postoperative computed tomography results between 3 and 27 months were collected. Volumetric analysis was performed to evaluate any differences in the rate of volumetric change according to the sex, age, initial volume of the lesion, duration, location of the lesion, tooth extraction, expansion of the cortical layer, and pathological diagnosis. Multiple linear regression and generalised linear mixed models were used for statistical analyses. Results: The mean reduction rate among all patients was 54.68%. Multiple linear regression analysis revealed that higher reduction rates were associated with a long decompression period, young patient age, and location of the cyst in the posterior maxilla. Generalised linear mixed models revealed that higher reduction rates were associated with a long decompression period and young patient age. Conclusions: Decompression was an effective treatment for reducing the cyst size in all patients. Its effectiveness increased with a long treatment duration, young patient age, and cyst location in the posterior maxilla three-dimensionally.

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Spectrally Resolved Fluxes from IASI Data: Retrieval Algorithm for Clear-Sky Measurements

Journal of Climate ◽

10.1175/jcli-d-19-0523.1 ◽

2020 ◽

Vol 33 (16) ◽

pp. 6971-6988 ◽

Cited By ~ 1

Author(s):

Simon Whitburn ◽

Lieven Clarisse ◽

Sophie Bauduin ◽

Maya George ◽

Daniel Hurtmans ◽

...

Keyword(s):

Longwave Radiation ◽

Real Data ◽

Far Infrared ◽

Data Retrieval ◽

Detection Algorithm ◽

Retrieval Algorithm ◽

Tropical Region ◽

Cloud Detection ◽

Distribution Models ◽

Spectrally Resolved

AbstractSpace-based measurements of the outgoing longwave radiation (OLR) are essential for the study of Earth’s climate system. While the CERES instrument provides accurate measurements of this quantity, its measurements are not spectrally resolved. Here we present a high-resolution OLR product (sampled at 0.25 cm−1), derived from measurements of the IASI satellite sounder. The applied methodology relies on precalculated angular distribution models (ADMs). These are usually calculated for tens to hundreds of different scene types (characterized by surface and atmosphere parameters). To guarantee accurate results in the range 645–2300 cm−1 covered by IASI, we constructed ADMs for over 140 000 scenes. These were selected from one year of CAMS reanalysis data. A dissimilarity-based selection algorithm was applied to choose scenes as different from each other as possible, thereby maximizing the performance on real data, while keeping the number of scenes manageable. A comparison of the IASI OLR integrated over the 645–2300 cm−1 range was performed with the longwave broadband OLR products from CERES and the AIRS instrument. The latter are systematically higher due to the contribution of the far infrared to the total IR spectral range, but as expected exhibit generally high spatial correlations with the IASI OLR, except for some areas in the tropical region. We also compared the IASI OLR against the spectrally resolved OLR derived from AIRS. A good agreement was found above 1200 cm−1 while AIRS OLR appeared to be systematically higher in the atmospheric window region, likely related to differences in overpass time or to the use of a different cloud detection algorithm.

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Seasonal and Weather Effects on Rheumatoid Arthritis: Myth or Reality?

Pain Research and Management ◽

10.1155/2020/5763080 ◽

2020 ◽

Vol 2020 ◽

pp. 1-5

Author(s):

Hamida Azzouzi ◽

Linda Ichchou

Keyword(s):

Rheumatoid Arthritis ◽

Regression Analysis ◽

Linear Regression ◽

Multiple Linear Regression ◽

Atmospheric Pressure ◽

Meteorological Parameters ◽

Linear Regression Analysis ◽

Multiple Linear Regression Analysis ◽

Maximum Temperature ◽

Tender Joint Count

Introduction. Many of our rheumatic patients report that weather and seasons affect their symptoms. Objective. The purpose of this study was to examine the effect of meteorological parameters within seasons on rheumatoid arthritis (RA) symptoms. Methods. A retrospective longitudinal study from July 2017 to August 2018 was conducted. Data from three consultations for three seasons were collected and included a tender and swollen joint count, a disease activity score for 28 joints (DAS28), and patient’s pain assessment from their computerized medical record. The weather conditions (minimum and maximum temperature, precipitation, humidity, atmospheric pressure, and wind speed) registered during the same day of consultation for each patient were obtained. Then, the statistical correlation between each meteorological parameter and RA parameters was determined using the multiple linear regression analysis. Results. The data of 117 patients with a mean age of 50.45 ± 12.17 years were analyzed. The mean DAS28 at baseline was 2.44 ± 0.95. The winter in Oujda is cold (average temperature between 10°C and15°C) compared to summer (24.5°C–32.7°C). The spring is wetter with a 71% average humidity. Overall, the tender joint count was significantly correlated with hygrometry (p=0.027) in winter. A similar result was obtained in summer with precipitation (p=0.003). The pain intensity in the summer was negatively correlated with minimum temperatures and atmospheric pressure. However, there was no correlation between meteorological parameters and disease objective parameters for all seasons. Multiple linear regression analysis showed that weather parameters appeared to explain the variability in four RA predictors in the summer. No significant associations were observed in the spring. Conclusion. Our study supported the physicians’ assumption regarding the effect of climate on pain in RA patients.

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A multiple linear regression GIS module using spatial variables to model orographic rainfall

Journal of Hydroinformatics ◽

10.2166/hydro.2004.0004 ◽

2004 ◽

Vol 6 (1) ◽

pp. 39-56 ◽

Cited By ~ 14

Author(s):

S. Naoum ◽

I. K. Tsanis

Keyword(s):

Linear Regression ◽

Multiple Linear Regression ◽

Spatial Interpolation ◽

Three Dimensional ◽

Predictor Variables ◽

Test Case ◽

Linear Regression Method ◽

Linear Regression Models ◽

Orographic Rainfall ◽

Interpolation Techniques

This paper aims to document the development of a new GIS-based spatial interpolation module that adopts a multiple linear regression technique. The functionality of the GIS module is illustrated through a test case represented by the island of Crete, Greece, where the models generated were applied to locations where estimates of annual precipitation were required. The response variable is ‘precipitation’ and the predictor variables are ‘elevation’, ‘longitude’ and ‘latitude’, or any combination of these. The module is capable of performing a sequence of tasks which will eventually lead to an estimation of mean areal precipitation and the total volume of precipitation. In addition, it can generate up to nine predictor variables and their parameters, and can estimate areal rainfall for a user-specified three-dimensional extent. The developed module performed satisfactorily. Precipitation estimates at ungauged locations were obtained using the multiple linear regression method in addition to some conventional spatial interpolation techniques (i.e. IDW, Spline, Kriging, etc.). The multiple linear regression models provided better estimates than the other spatial interpolation techniques.

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The Use of Multiple Linear Regression Model for Predicting The Influence of Weather to The Prevalence of Newcastle Disease in Chicken in North Aceh

Jurnal Kedokteran Hewan - Indonesian Journal of Veterinary Sciences ◽

10.21157/j.ked.hewan.v2i1.9778 ◽

2008 ◽

Vol 2 (1) ◽

Author(s):

Mahdi Abrar

Keyword(s):

Linear Regression ◽

Regression Model ◽

Multiple Linear Regression ◽

Newcastle Disease ◽

Linear Regression Model ◽

Minimum Temperature ◽

Multiple Linear Regression Model ◽

Maximum Temperature ◽

Independent Variables ◽

Dependent Variables

The objective of this research is to see the influence of weather on the prevalence of Newcastle Disease (ND) in chicken in Kabupaten Aceh Utara (North Aceh). Data used in this research were obtained from Dinas Peternakan North Aceh for the number of chicken suffered ND and from Badan Meteorologi dan Geofisika Lhokseumawe, North Aceh for the form of weather. Multiple Linear Regression Model with five independent variables (the average of rainfall per month, the average of maximum temperature, the average of minimum temperature, the velocity of the wind, and the average of humidity per month) was used to see the influence of wheather to the prevalence of Newcastle Disease. Proportion the number of chicken suffered from ND which is the ratio of the number of chicken suffered from ND to the total number of chicken was used as dependent variables. The result shows that the best model is Ŷ= 120.529278 – 1.33 x wind humidity + 1.907 x wind velocity.

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