Uncertainties of ground-based microwave radiometer retrievals in zenith and off-zenith methods under snow conditions

Abstract. This paper is to investigate the uncertainties of microwave radiometer (MWR) retrievals in snow conditions and also explore the discrepancies of MWR retrievals in zenith and off-zenith methods. The MWR retrievals were averaged in the ±15 min period centered at sounding times of 00:00 and 12:00 UTC and compared with the radiosonde observations (RAOBs). In general, the MWR retrievals have a better correlation with RAOB profiles in off-zenith method than in zenith method, and the biases (MWR observations minus RAOBs) and root mean square errors (RMSEs) between MWR and RAOB are also clearly reduced in off-zenith method. The biases of temperature, relative humidity, and vapor density decrease from 4.6 K, 9 %, and 1.43 g m−3 in zenith method to −0.6 K, −2 %, and 0.10 g m−3 in off-zenith method, respectively. The discrepancies between the MWR retrievals and the RAOB profiles along with the altitude present the same situation. Case studies show that the impact of snow on accuracies of the MWR retrievals is more serious in heavy snowfall than that in light snowfall, but the off-zenith method can mitigate the impact of snowfall. The MWR measurements become less accurate in snowfall is mainly due to the retrieving method which does not consider the effect of snow, and the accumulated snow on the top of radome increases the signal noise of MWR measurement. As the snowfall drops away by gravity in the sides of the radome and the off-zenith observations are more representative of the atmospheric conditions for RAOBs.

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Uncertainties of ground-based microwave radiometer retrievals in zenith and off-zenith observations under snow conditions

Atmospheric Measurement Techniques ◽

10.5194/amt-10-155-2017 ◽

2017 ◽

Vol 10 (1) ◽

pp. 155-165 ◽

Cited By ~ 1

Author(s):

Wengang Zhang ◽

Guirong Xu ◽

Yuanyuan Liu ◽

Guopao Yan ◽

Dejun Li ◽

...

Keyword(s):

Relative Humidity ◽

Microwave Radiometer ◽

Retrieval Algorithm ◽

Mean Square ◽

Atmospheric Conditions ◽

Vapor Density ◽

Heavy Snowfall ◽

Snow Conditions ◽

The Impact ◽

Mean Square Errors

Abstract. This paper is to investigate the uncertainties of microwave radiometer (MWR) retrievals in snow conditions and also explore the discrepancies of MWR retrievals in zenith and off-zenith observations. The MWR retrievals were averaged in a ±15 min period centered at sounding times of 00:00 and 12:00 UTC and compared with radiosonde observations (RAOBs). In general, the MWR retrievals have a better correlation with RAOB profiles in off-zenith observations than in zenith observations, and the biases (MWR observations minus RAOBs) and root mean square errors (RMSEs) between MWR and RAOB are also clearly reduced in off-zenith observations. The biases of temperature, relative humidity, and vapor density decrease from 4.6 K, 9 %, and 1.43 g m−3 in zenith observations to −0.6 K, −2 %, and 0.10 g m−3 in off-zenith observations, respectively. The discrepancies between MWR retrievals and RAOB profiles by altitude present the same situation. Cases studies show that the impact of snow on accuracies of MWR retrievals is more serious in heavy snowfall than in light snowfall, but off-zenith observation can mitigate the impact of snowfall. The MWR measurements become less accurate in snowfall mainly due to the retrieval algorithm, which does not consider the effect of snow, and the accumulated snow on the top of the radome increases the signal noise of MWR measurements. As the snowfall drops away by gravity on the sides of the radome, the off-zenith observations are more representative of the atmospheric conditions for RAOBs.

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Minimum Local Emissivity Variance Retrieval of Cloud Altitude and Effective Spectral Emissivity—Simulation and Initial Verification

Journal of Applied Meteorology ◽

10.1175/2090.1 ◽

2004 ◽

Vol 43 (5) ◽

pp. 795-809 ◽

Cited By ~ 23

Author(s):

Hung-Lung Huang ◽

William L. Smith ◽

Jun Li ◽

Paolo Antonelli ◽

Xiangqian Wu ◽

...

Keyword(s):

Root Mean Square ◽

Spectral Resolution ◽

Single Layer ◽

Spectral Emissivity ◽

Pressure Level ◽

High Spectral Resolution ◽

Mean Square ◽

Atmospheric Conditions ◽

Wide Range ◽

Mean Square Errors

Abstract This paper describes the theory and application of the minimum local emissivity variance (MLEV) technique for simultaneous retrieval of cloud pressure level and effective spectral emissivity from high-spectral-resolution radiances, for the case of single-layer clouds. This technique, which has become feasible only with the recent development of high-spectral-resolution satellite and airborne instruments, is shown to provide reliable cloud spectral emissivity and pressure level under a wide range of atmospheric conditions. The MLEV algorithm uses a physical approach in which the local variances of spectral cloud emissivity are calculated for a number of assumed or first-guess cloud pressure levels. The optimal solution for the single-layer cloud emissivity spectrum is that having the “minimum local emissivity variance” among the retrieved emissivity spectra associated with different first-guess cloud pressure levels. This is due to the fact that the absorption, reflection, and scattering processes of clouds exhibit relatively limited localized spectral emissivity structure in the infrared 10–15-μm longwave region. In this simulation study it is shown that the MLEV cloud pressure root-mean-square errors for a single level with effective cloud emissivity greater than 0.1 are ∼30, ∼10, and ∼50 hPa, for high (200– 300 hPa), middle (500 hPa), and low (850 hPa) clouds, respectively. The associated cloud emissivity root-mean-square errors in the 900 cm−1 spectral channel are less than 0.05, 0.04, and 0.25 for high, middle, and low clouds, respectively.

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The Analysis of Atmospheric Water Vapor Based on Ground-Based Microwave Radiometer

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.500.335 ◽

2012 ◽

Vol 500 ◽

pp. 335-340

Author(s):

Jie Ying He ◽

Feng Lin Sun ◽

Sheng Wei Zhang ◽

Yu Zhang

Keyword(s):

Neural Network ◽

Water Vapor ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Microwave Radiometer ◽

Mean Square ◽

Vapor Density ◽

China Meteorological Administration ◽

Density Profiles

The paper introduces a widely used atmospheric absorption models: MPM by Liebe in 1989. Using this absorption model, the paper simulates the temperature and humidity weighting functions and brightness temperature according to the different frequencies and bandwidth of the multi-channel ground-based microwave radiometer. The results show that simulated brightness temperatures are very well agreement with the observation values with an acceptable root mean square error. This paper uses widely used retrieval method of artificial neural network to obtain the water vapor density profiles and calculates the root mean square error of each dataset. Also, to improve the accuracy of retrievals, this paper adopts multi-layers neural network which has two hidden layers. The results show that the retrievals of water vapor density profiles based on ground-based microwave radiometer are agreement with the water vapor density profile which is observed by radiosonde. Grant Nos. GYHY200906035 China Meteorological Administration nonprofit sector (meteorology) special research

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Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽

10.3390/foods10040885 ◽

2021 ◽

Vol 10 (4) ◽

pp. 885

Author(s):

Sergio Ghidini ◽

Luca Maria Chiesa ◽

Sara Panseri ◽

Maria Olga Varrà ◽

Adriana Ianieri ◽

...

Keyword(s):

Root Mean Square ◽

Near Infrared ◽

Nir Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Coefficient Of Determination ◽

Mean Square ◽

Tuna Fish ◽

Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

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Examining the Impact of and Sensitivity of Fit Indices to Omitting Covariates Interaction Effect in Multilevel Multiple-Indicator Multiple-Cause Models

Educational and Psychological Measurement ◽

10.1177/0013164421992407 ◽

2021 ◽

pp. 001316442199240

Author(s):

Chunhua Cao ◽

Eun Sook Kim ◽

Yi-Hsin Chen ◽

John Ferron

Keyword(s):

Root Mean Square ◽

Interaction Effect ◽

Intraclass Correlation ◽

Information Criteria ◽

Parameter Estimates ◽

Mean Square ◽

Fit Indices ◽

Cluster Number ◽

Multiple Indicator ◽

The Impact

This study examined the impact of omitting covariates interaction effect on parameter estimates in multilevel multiple-indicator multiple-cause models as well as the sensitivity of fit indices to model misspecification when the between-level, within-level, or cross-level interaction effect was left out in the models. The parameter estimates produced in the correct and the misspecified models were compared under varying conditions of cluster number, cluster size, intraclass correlation, and the magnitude of the interaction effect in the population model. Results showed that the two main effects were overestimated by approximately half of the size of the interaction effect, and the between-level factor mean was underestimated. None of comparative fit index, Tucker–Lewis index, root mean square error of approximation, and standardized root mean square residual was sensitive to the omission of the interaction effect. The sensitivity of information criteria varied depending majorly on the magnitude of the omitted interaction, as well as the location of the interaction (i.e., at the between level, within level, or cross level). Implications and recommendations based on the findings were discussed.

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Evidence That Supertriangles Exist in Nature from the Vertical Projections of Koelreuteria paniculata Fruit

Symmetry ◽

10.3390/sym14010023 ◽

2021 ◽

Vol 14 (1) ◽

pp. 23

Author(s):

Yuping Li ◽

Brady K. Quinn ◽

Johan Gielis ◽

Yirong Li ◽

Peijian Shi

Keyword(s):

Root Mean Square ◽

Goodness Of Fit ◽

Mean Square ◽

Sea Stars ◽

Different Types ◽

Koelreuteria Paniculata ◽

Two Parameters ◽

Mean Square Errors

Many natural radial symmetrical shapes (e.g., sea stars) follow the Gielis equation (GE) or its twin equation (TGE). A supertriangle (three triangles arranged around a central polygon) represents such a shape, but no study has tested whether natural shapes can be represented as/are supertriangles or whether the GE or TGE can describe their shape. We collected 100 pieces of Koelreuteria paniculata fruit, which have a supertriangular shape, extracted the boundary coordinates for their vertical projections, and then fitted them with the GE and TGE. The adjusted root mean square errors (RMSEadj) of the two equations were always less than 0.08, and >70% were less than 0.05. For 57/100 fruit projections, the GE had a lower RMSEadj than the TGE, although overall differences in the goodness of fit were non-significant. However, the TGE produces more symmetrical shapes than the GE as the two parameters controlling the extent of symmetry in it are approximately equal. This work demonstrates that natural supertriangles exist, validates the use of the GE and TGE to model their shapes, and suggests that different complex radially symmetrical shapes can be generated by the same equation, implying that different types of biological symmetry may result from the same biophysical mechanisms.

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Impact of different centroid means on the accuracy of orthometric height modelling by geometric geoid method

International Journal of Scientific Reports ◽

10.18203/issn.2454-2156.intjscirep20201267 ◽

2020 ◽

Vol 6 (4) ◽

pp. 124 ◽

Cited By ~ 1

Author(s):

Oluyori P. Dare ◽

Eteje S. Okiemute

Keyword(s):

Root Mean Square ◽

Arithmetic Mean ◽

Orthometric Height ◽

Harmonic Mean ◽

Geometric Method ◽

Mean Square ◽

Geoid Model ◽

Microsoft Excel ◽

Orthometric Heights ◽

The Impact

Background: Orthometric height, as well as geoid modelling using the geometric method, requires centroid computation. And this can be obtained using various models, as well as methods. These methods of centroid mean computation have impacts on the accuracy of the geoid model since the basis of the development of the theory of each centroid mean type is different. This paper presents the impact of different centroid means on the accuracy of orthometric height modelling by geometric geoid method.Methods: DGPS observation was carried out to obtain the coordinates and ellipsoidal heights of selected points. The centroid means were computed with the coordinates using three different centroid means models (arithmetic mean, root mean square and harmonic mean). The computed centroid means were entered accordingly into a Microsoft Excel program developed using the Multiquadratic surface to obtain the model orthometric heights at various centroid means. The root means square error (RMSE) index was applied to obtain the accuracy of the model using the known and the model orthometric heights obtained at various centroid means. Results: The computed accuracy shows that the arithmetic mean method is the best among the three centroid means types.Conclusions: It is concluded that the arithmetic mean method should be adopted for centroid computation, as well as orthometric height modelling using the geometric method.

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The Accuracy of Real-Time hmF2 Estimation from Ionosondes

Remote Sensing ◽

10.3390/rs12172671 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2671

Author(s):

Carlo Scotto ◽

Dario Sabbagh

Keyword(s):

Data Analysis ◽

Real Time ◽

Root Mean Square ◽

Magnetic Activity ◽

Mean Square ◽

Incoherent Scatter Radar ◽

Incoherent Scatter ◽

Scatter Radar ◽

Mean Square Errors

A total of 4991 ionograms recorded from April 1997 to December 2017 by the Millstone Hill Digisonde (42.6°N, 288.5°E) were considered, with simultaneous Ne(h)[ISR] profiles recorded by the co-located Incoherent Scatter Radar (ISR). The entire ionogram dataset was scaled with both the Autoscala and ARTIST programs. The reliability of the hmF2 values obtained by ARTIST and Autoscala was assessed using the corresponding ISR values as a reference. Average errors Δ and the root mean square errors RMSE were computed for the whole dataset. Data analysis shows that both the Autoscala and ARTIST systems tend to underestimate hmF2 values with |Δ| in all cases less than 10 km. For high magnetic activity ARTIST offers better accuracy than Autoscala, as evidenced by RMSE[ARTIST] < RMSE[Autoscala], under both daytime and nighttime conditions, and considering all hours of the day. Conversely, under low and medium magnetic activity Autoscala tends to estimate hmF2 more accurately than the ARTIST system for both daytime and nighttime conditions, when RMSE[Autoscala] < RMSE[ARTIST]. However, RMSE[Autoscala] slightly exceeds RMSE[ARTIST] for the day as a whole. RMSE values are generally substantial (RMSE > 16 km in all cases), which places a limit on the results obtainable with real-time models that ingest ionosonde data.

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Validation of ESA Sentinel-2 L2A Aerosol Optical Thickness and Columnar Water Vapour during 2017–2018

Remote Sensing ◽

10.3390/rs11141649 ◽

2019 ◽

Vol 11 (14) ◽

pp. 1649 ◽

Cited By ~ 2

Author(s):

María Ángeles Obregón ◽

Gonçalo Rodrigues ◽

Maria Joao Costa ◽

Miguel Potes ◽

Ana Maria Silva

Keyword(s):

Water Vapour ◽

Root Mean Square ◽

Optical Thickness ◽

Spectral Response ◽

Aerosol Optical Thickness ◽

Mean Bias Error ◽

Mean Square ◽

Response Functions ◽

Mean Square Errors ◽

Sentinel 2

This study presents a validation of aerosol optical thickness (AOT) and integrated water vapour (IWV) products provided by the European Space Agency (ESA) from multi-spectral imager (MSI) measurements on board the Sentinel-2 satellite (ESA-L2A). For that purpose, data from 94 Aerosol Robotic Network (AERONET) stations over Europe and adjacent regions, covering a wide geographical region with a variety of climate and environmental conditions and during the period between March 2017 and December 2018 have been used. The comparison between ESA-L2A and AERONET shows a better agreement for IWV than the AOT, with normalized root mean square errors (NRMSE) of 5.33% and 9.04%, respectively. This conclusion is also reflected in the values of R2, which are 0.99 and 0.65 for IWV and AOT, respectively. The study period was divided into two sub-periods, before and after 15 January 2018, when the Sentinel-2A spectral response functions of bands 1 and 2 (centered at 443 and 492 nm) were updated by ESA, in order to investigate if the lack of agreement in the AOT values was connected to the use of incorrect spectral response functions. The comparison of ESA-L2A AOT with AERONET measurements showed a better agreement for the second sub-period, with root mean square error (RMSE) values of 0.08 in comparison with 0.14 in the first sub-period. This same conclusion was attained considering mean bias error (MBE) values that decreased from 0.09 to 0.01. The ESA-L2A AOT values estimated with the new spectral response functions were closer to the correspondent reference AERONET values than the ones obtained using the previous spectral response functions. IWV was not affected by this change since the retrieval algorithm does not use bands 1 and 2 of Sentinel-2. Additionally, an analysis of potential uncertainty sources to several factors affecting the AOT comparison is presented and recommendations regarding the use of ESA-L2A AOT dataset are given.

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A STUDY OF MODELS FOR HANDGRIP FORCE PREDICTION FROM SURFACE ELECTROMYOGRAPHY OF EXTENSOR MUSCLE

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s1016237209001131 ◽

2009 ◽

Vol 21 (02) ◽

pp. 81-88 ◽

Cited By ~ 1

Author(s):

Wensheng Hou ◽

Xiaolin Zheng ◽

Yingtao Jiang ◽

Jun Zheng ◽

Chenglin Peng ◽

...

Keyword(s):

Root Mean Square ◽

Surface Electromyography ◽

Random Sequence ◽

Maximum Voluntary Contraction ◽

Force Production ◽

Voluntary Contraction ◽

Mean Square ◽

Force Level ◽

Force Output ◽

Mean Square Errors

Force production involves the coordination of multiple muscles, and the produced force levels can be attributed to the electrophysiology activities of those related muscles. This study is designed to explore the activity modes of extensor carpi radialis longus (ECRL) using surface electromyography (sEMG) at the presence of different handgrip force levels. We attempt to compare the performance of both the linear and nonlinear models for estimating handgrip forces. To achieve this goal, a pseudo-random sequence of handgrip tasks with well controlled force ranges is defined for calibration. Eight subjects (all university students, five males, and three females) have been recruited to conduct both calibration and voluntary trials. In each trial, sEMG signals have been acquired and preprocessed with Root–Mean–Square (RMS) method. The preprocessed signals are then normalized with amplitude value of Maximum Voluntary Contraction (MVC)-related sEMG. With the sEMG data from calibration trials, three models, Linear, Power, and Logarithmic, are developed to correlate the handgrip force output with the sEMG activities of ECRL. These three models are subsequently employed to estimate the handgrip force production of voluntary trials. For different models, the Root–Mean–Square–Errors (RMSEs) of the estimated force output for all the voluntary trials are statistically compared in different force ranges. The results show that the three models have different performance in different force ranges. Linear model is suitable for moderate force level (30%–50% MVC), whereas a nonlinear model is more accurate in the weak force level (Power model, 10%–30% MVC) or the strong force level (Logarithmic model, 50%–80% MVC).

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