scholarly journals Global component analysis of errors in five satellite-only global precipitation estimates

2020 ◽  
Author(s):  
Hanqing Chen ◽  
Bin Yong ◽  
Leyang Wang ◽  
Liliang Ren ◽  
Yang Hong
Keyword(s):  
Systematic Error ◽  
Random Error ◽  
Mean Squared Error ◽  
Systematic Errors ◽  
Topographic Factor ◽  
Error Adjustment ◽  
Error Components ◽  
Four Seasons ◽  
Retrieval Algorithms ◽  
The Impact

Abstract. Revealing the error components for satellite-only precipitation products (SPPs) can help algorithm developers and end-users substantially understand their error features and meanwhile is fundamental to customize retrieval algorithms and error adjustment models. Two error decomposition schemes were employed to explore the error components for five SPPs (i.e., MERG-Late, IMERG-Early, GSMaP-MVK, GSMaP-NRT, and PERSIANN-CCS) over different seasons, rainfall intensities, and topography classes. Firstly, this study depicted global maps of the total bias (total mean squared error) and its three (two) independent components for these five SPPs over four seasons for the first time. We found that the evaluation results between similar regions could not be extended to one another. Hit and/or false biases are major components of the total bias in most regions of the global land areas. In addition, the proportions of the systematic error are less than 20 % of total errors in most areas. One should note that each SPP has larger systematic errors in several regions (i.e., Russia, China, and Conterminous United States) for all four seasons, these larger systematic errors from retrieval algorithms are primarily due to the missed precipitation. Furthermore, IMERG suite and GSMaP-NRT display less systematic error in the rain rates with intensity less than 40 mm/day, while the systematic errors of GSMaP-MVK and PERSIANN-CCS increase with increasing rainfall intensity. Given that mean elevation cannot reflect the complex degree of terrain, we introduced the standard deviation of elevation (SDE) to replace mean elevation to better describe topographic complexity. Compared with other SPPs, GSMaP suite shows a stronger topographic dependency in the four bias scores. A novel metric namely normalized error component (NEC) was proposed to fairly evaluate the impact of the solely topographic factor on systematic (random) error. It is found that these products show different topographic dependency patterns in systematic (random) error. Meanwhile, the pattern of the impact of the solely topographic factor on systematic (random) error is similar to the relationship between systematic (random) error and topography because the average precipitations of all topography categories are very close. Finally, the potential directions of the improvement in satellite precipitation retrieval algorithms and error adjustment models were identified in this study.

scholarly journals Global component analysis of errors in three satellite-only global precipitation estimates

2021 ◽  
Vol 25 (6) ◽  
pp. 3087-3104
Author(s):  
Hanqing Chen ◽  
Bin Yong ◽  
Pierre-Emmanuel Kirstetter ◽  
Leyang Wang ◽  
Yang Hong
Keyword(s):  
Mean Squared Error ◽  
Total Error ◽  
Error Component ◽  
Random Errors ◽  
Error Components ◽  
Precipitation Estimates ◽  
Different Seasons ◽  
Systematic And Random Errors ◽  
Potential Methods ◽  
The Impact

Abstract. Revealing the error components of satellite-only precipitation products (SPPs) can help algorithm developers and end-users understand their error features and improve retrieval algorithms. Here, two error decomposition schemes are employed to explore the error components of the IMERG-Late, GSMaP-MVK, and PERSIANN-CCS SPPs over different seasons, rainfall intensities, and topography classes. Global maps of the total bias (total mean squared error) and its three (two) independent components are depicted for the first time. The evaluation results for similar regions are discussed, and it is found that the evaluation results for one region cannot be extended to another similar region. Hit and/or false biases are the major components of the total bias in most overland regions globally. The systematic error contributes less than 20 % of the total error in most areas. Large systematic errors are primarily due to missed precipitation. It is found that the SPPs show different topographic patterns in terms of systematic and random errors. Notably, among the SPPs, GSMaP-MVK shows the strongest topographic dependency of the four bias scores. A novel metric, namely the normalized error component (NEC), is proposed as a means to isolate the impact of topography on the systematic and random errors. Potential methods of improving satellite precipitation retrievals and error adjustment models are discussed.

B-cell and T-cell values in peripheral blood in Polish mixed-breed rabbits with addition of blood of meet breeds

2014 ◽  
Vol 17 (3) ◽  
pp. 421-426 ◽  
Author(s):  
B. Tokarz-Deptuła ◽  
P. Niedźwiedzka-Rystwej ◽  
B. Hukowska-Szematowicz ◽  
M. Adamiak ◽  
A. Trzeciak-Ryczek ◽  
...  
Keyword(s):  
T Cells ◽  
B Cells ◽  
B Cell ◽  
Cd8 T Cells ◽  
Peripheral Blood ◽  
Laboratory Animals ◽  
Immunological Parameters ◽  
Four Seasons ◽  
Mixed Breed ◽  
The Impact

Abstract In Poland, rabbit is a highly valued animal, due to dietetic and flavour values of its meat, but above all, rabbits tend to be commonly used laboratory animals. The aim of the study was developing standards for counts of B-cells with CD19+ receptor, T-cells with CD5+ receptor, and their subpopulations, namely T-cells with CD4+, CD8+ and CD25+ receptor in the peripheral blood of mixed-breed Polish rabbits with addition of blood of meet breeds, including the assessment of the impact of four seasons of the year and animal sex on the values of the immunological parameters determined. The results showed that the counts of B- and T-cells and their subpopulations in peripheral blood remain within the following ranges: for CD19+ B-cells: 1.05 - 3.05%, for CD5+ T-cells: 34.00 - 43.07%, CD4+ T-cells: 23.52 - 33.23%, CD8+ T-cells: 12.55 - 17.30%, whereas for CD25+ T-cells: 0.72 - 2.81%. As it comes to the season of the year, it was observed that it principally affects the values of CD25+ T-cells, while in the case of rabbit sex, more changes were found in females.

scholarly journals Evaluation of P-Band SAR Tomography for Mapping Tropical Forest Vertical Backscatter and Tree Height

2021 ◽  
Vol 13 (8) ◽  
pp. 1485
Author(s):  
Naveen Ramachandran ◽  
Sassan Saatchi ◽  
Stefano Tebaldini ◽  
Mauro Mariotti d’Alessandro ◽  
Onkar Dikshit
Keyword(s):  
Tropical Forest ◽  
Vertical Profile ◽  
Mean Squared Error ◽  
Low Frequency ◽  
Tree Height ◽  
Canopy Layer ◽  
Local Maxima ◽  
Slope Correction ◽  
Compensation Approach ◽  
The Impact

Low-frequency tomographic synthetic aperture radar (TomoSAR) techniques provide an opportunity for quantifying the dynamics of dense tropical forest vertical structures. Here, we compare the performance of different TomoSAR processing, Back-projection (BP), Capon beamforming (CB), and MUltiple SIgnal Classification (MUSIC), and compensation techniques for estimating forest height (FH) and forest vertical profile from the backscattered echoes. The study also examines how polarimetric measurements in linear, compact, hybrid, and dual circular modes influence parameter estimation. The tomographic analysis was carried out using P-band data acquired over the Paracou study site in French Guiana, and the quantitative evaluation was performed using LiDAR-based canopy height measurements taken during the 2009 TropiSAR campaign. Our results show that the relative root mean squared error (RMSE) of height was less than 10%, with negligible systematic errors across the range, with Capon and MUSIC performing better for height estimates. Radiometric compensation, such as slope correction, does not improve tree height estimation. Further, we compare and analyze the impact of the compensation approach on forest vertical profiles and tomographic metrics and the integrated backscattered power. It is observed that radiometric compensation increases the backscatter values of the vertical profile with a slight shift in local maxima of the canopy layer for both the Capon and the MUSIC estimators. Our results suggest that applying the proper processing and compensation techniques on P-band TomoSAR observations from space will allow the monitoring of forest vertical structure and biomass dynamics.

scholarly journals Blending and obscuration in weak lensing magnification

2021 ◽  
Author(s):  
E Gaztanaga ◽  
S J Schmidt ◽  
M D Schneider ◽  
J A Tyson
Keyword(s):  
Systematic Errors ◽  
Weak Lensing ◽  
Wide Field ◽  
Photometric Redshifts ◽  
Field Surveys ◽  
Systematic Effects ◽  
The Impact ◽  
Close Pairs ◽  
Mass Profiles

Abstract We test the impact of some systematic errors in weak lensing magnification measurements with the COSMOS 30-band photo-z Survey flux limited to Iauto < 25.0 using correlations of both source galaxy counts and magnitudes. Systematic obscuration effects are measured by comparing counts and magnification correlations. We use the ACS-HST catalogs to identify potential blending objects (close pairs) and perform the magnification analyses with and without blended objects. We find that blending effects start to be important (∼ 0.04 mag obscuration) at angular scales smaller than 0.1 arcmin. Extinction and other systematic obscuration effects can be as large as 0.10 mag (U-band) but are typically smaller than 0.02 mag depending on the band. After applying these corrections, we measure a 3.9σ magnification signal that is consistent for both counts and magnitudes. The corresponding projected mass profiles of galaxies at redshift z ≃ 0.6 (MI ≃ −21) is Σ = 25 ± 6M⊙h3/pc2 at 0.1 Mpc/h, consistent with NFW type profile with M200 ≃ 2 × 1012M⊙h/pc2. Tangential shear and flux-size magnification over the same lenses show similar mass profiles. We conclude that magnification from counts and fluxes using photometric redshifts has the potential to provide complementary weak lensing information in future wide field surveys once we carefully take into account systematic effects, such as obscuration and blending.

scholarly journals Technical note: Boundary layer height determination from Lidar for improving air pollution episode modelling: development of new algorithm and evaluation

2017 ◽  
Author(s):  
Ting Yang ◽  
Zifa Wang ◽  
Wei Zhang ◽  
Alex Gbaguidi ◽  
Nubuo Sugimoto ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Air Pollution ◽  
High Performance ◽  
Accurate Determination ◽  
Technical Note ◽  
Strong Increase ◽  
Layer Height ◽  
Boundary Layer Height ◽  
Retrieval Algorithms ◽  
The Impact

Abstract. Predicting air pollution events in low atmosphere over megacities requires thorough understanding of the tropospheric dynamic and chemical processes, involving notably, continuous and accurate determination of the boundary layer height (BLH). Through intensive observations experimented over Beijing (China), and an exhaustive evaluation existing algorithms applied to the BLH determination, persistent critical limitations are noticed, in particular over polluted episodes. Basically, under weak thermal convection with high aerosol loading, none of the retrieval algorithms is able to fully capture the diurnal cycle of the BLH due to pollutant insufficient vertical mixing in the boundary layer associated with the impact of gravity waves on the tropospheric structure. Subsequently, a new approach based on gravity wave theory (the cubic root gradient method: CRGM), is developed to overcome such weakness and accurately reproduce the fluctuations of the BLH under various atmospheric pollution conditions. Comprehensive evaluation of CRGM highlights its high performance in determining BLH from Lidar. In comparison with the existing retrieval algorithms, the CRGM potentially reduces related computational uncertainties and errors from BLH determination (strong increase of correlation coefficient from 0.44 to 0.91 and significant decrease of the root mean square error from 643 m to 142 m). Such newly developed technique is undoubtedly expected to contribute to improve the accuracy of air quality modelling and forecasting systems.

scholarly journals When does the use of individual patient data in network meta-analysis make a difference? A simulation study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Steve Kanters ◽  
Mohammad Ehsanul Karim ◽  
Kristian Thorlund ◽  
Aslam Anis ◽  
Nick Bansback
Keyword(s):  
Simulation Study ◽  
Treatment Effect ◽  
Mean Squared Error ◽  
Individual Patient Data ◽  
Meta Analysis ◽  
Model Performance ◽  
Patient Data ◽  
Meta Regression ◽  
Treatment Comparisons ◽  
The Impact

Abstract Background The use of individual patient data (IPD) in network meta-analyses (NMA) is rapidly growing. This study aimed to determine, through simulations, the impact of select factors on the validity and precision of NMA estimates when combining IPD and aggregate data (AgD) relative to using AgD only. Methods Three analysis strategies were compared via simulations: 1) AgD NMA without adjustments (AgD-NMA); 2) AgD NMA with meta-regression (AgD-NMA-MR); and 3) IPD-AgD NMA with meta-regression (IPD-NMA). We compared 108 parameter permutations: number of network nodes (3, 5 or 10); proportion of treatment comparisons informed by IPD (low, medium or high); equal size trials (2-armed with 200 patients per arm) or larger IPD trials (500 patients per arm); sparse or well-populated networks; and type of effect-modification (none, constant across treatment comparisons, or exchangeable). Data were generated over 200 simulations for each combination of parameters, each using linear regression with Normal distributions. To assess model performance and estimate validity, the mean squared error (MSE) and bias of treatment-effect and covariate estimates were collected. Standard errors (SE) and percentiles were used to compare estimate precision. Results Overall, IPD-NMA performed best in terms of validity and precision. The median MSE was lower in the IPD-NMA in 88 of 108 scenarios (similar results otherwise). On average, the IPD-NMA median MSE was 0.54 times the median using AgD-NMA-MR. Similarly, the SEs of the IPD-NMA treatment-effect estimates were 1/5 the size of AgD-NMA-MR SEs. The magnitude of superior validity and precision of using IPD-NMA varied across scenarios and was associated with the amount of IPD. Using IPD in small or sparse networks consistently led to improved validity and precision; however, in large/dense networks IPD tended to have negligible impact if too few IPD were included. Similar results also apply to the meta-regression coefficient estimates. Conclusions Our simulation study suggests that the use of IPD in NMA will considerably improve the validity and precision of estimates of treatment effect and regression coefficients in the most NMA IPD data-scenarios. However, IPD may not add meaningful validity and precision to NMAs of large and dense treatment networks when negligible IPD are used.

Prognosis of water levels in a moor groundwater system influenced by hydrology and water extraction using an artificial neural network 

2021 ◽  
Author(s):  
Sascha Flaig ◽  
Timothy Praditia ◽  
Alexander Kissinger ◽  
Ulrich Lang ◽  
Sergey Oladyshkin ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Squared Error ◽  
Meteorological Data ◽  
Water Levels ◽  
Typical Structure ◽  
Water Abstraction ◽  
Physical Knowledge ◽  
Artificial Neural ◽  
The Impact

<p>In order to prevent possible negative impacts of water abstraction in an ecologically sensitive moor south of Munich (Germany), a “predictive control” scheme is in place. We design an artificial neural network (ANN) to provide predictions of moor water levels and to separate hydrological from anthropogenic effects. As the moor is a dynamic system, we adopt the „Long short-term memory“ architecture.</p><p>To find the best LSTM setup, we train, test and compare LSTMs with two different structures: (1) the non-recurrent one-to-one structure, where the series of inputs are accumulated and fed into the LSTM; and (2) the recurrent many-to-many structure, where inputs gradually enter the LSTM (including LSTM forecasts from previous forecast time steps). The outputs of our LSTMs then feed into a readout layer that converts the hidden states into water level predictions. We hypothesize that the recurrent structure is the better structure because it better resembles the typical structure of differential equations for dynamic systems, as they would usually be used for hydro(geo)logical systems. We evaluate the comparison with the mean squared error as test metric, and conclude that the recurrent many-to-many LSTM performs better for the analyzed complex situations. It also produces plausible predictions with reasonable accuracy for seven days prediction horizon.</p><p>Furthermore, we analyze the impact of preprocessing meteorological data to evapotranspiration data using typical ETA models. Inserting knowledge into the LSTM in the form of ETA models (rather than implicitly having the LSTM learn the ETA relations) leads to superior prediction results. This finding aligns well with current ideas on physically-inspired machine learning.</p><p>As an additional validation step, we investigate whether our ANN is able to correctly identify both anthropogenic and natural influences and their interaction. To this end, we investigate two comparable pumping events under different meteorological conditions. Results indicate that all individual and combined influences of input parameters on water levels can be represented well. The neural networks recognize correctly that the predominant precipitation and lower evapotranspiration during one pumping event leads to a lower decrease of the hydrograph.</p><p>To further demonstrate the capability of the trained neural network, scenarios of pumping events are created and simulated.</p><p>In conclusion, we show that more robust and accurate predictions of moor water levels can be obtained if available physical knowledge of the modeled system is used to design and train the neural network. The artificial neural network can be a useful instrument to assess the impact of water abstraction by quantifying the anthropogenic influence.</p>

scholarly journals Comparison of space analysis performed on plaster vs. digital dental casts applying Tanaka and Johnston's equation

2013 ◽  
Vol 18 (1) ◽  
pp. 128-133 ◽  
Author(s):  
Júlia Olien Sanches ◽  
Lourdes Aparecida Martins dos Santos-Pinto ◽  
Ary dos Santos-Pinto ◽  
Betina Grehs ◽  
Fabiano Jeremias
Keyword(s):  
Systematic Error ◽  
Random Error ◽  
Regression Equation ◽  
Predictive Values ◽  
Error Bias ◽  
Digital Caliper ◽  
Plaster Casts ◽  
Paired T Test ◽  
Lower Incisors ◽  
Dental Casts

OBJECTIVE: The purpose of this study was to compare dental size measurements, their reproducibility and the application of Tanaka and Johnston regression equation in predicting the size of canines and premolars on plaster and digital dental casts. METHODS: Thirty plaster casts were scanned and digitized. Mesiodistal measurements of the teeth were then performed with a digital caliper on the plaster and digital casts using O3d software system (Widialabs©).The sum of the sizes of the lower incisors was used to obtain predictive values of the sizes of the premolars and canines using the regression equation, and these values were compared with the actual sizes of the teeth. The data were statistically analyzed by applying to the results Pearson's correlation test, Dahlberg's formula, paired t-test and analysis of variance (p<0.05). RESULTS: Excellent intraexaminer agreement was observed in the measurements performed on both dental casts. No random error was present in the measurements obtained with the caliper and systematic error (bias) was more frequent in the digital casts. Space prediction obtained by applying the regression equation was greater than the sum of the canines and premolars on the plaster and digital casts. CONCLUSIONS: Despite an adequate reproducibility of the measurements performed on both casts, most measurements on the digital casts were higher than those on the plaster casts. The predicted space was overestimated in both models and significantly higher in the digital casts.

scholarly journals STUDI PERAMBATAN KESALAHAN SISTEMATIS LUAS LAHAN PARKIR KAMPUS MENGGUNAKAN PENDEKATAN LUAS SEGITIGA SEMBARANG

2019 ◽  
Vol 13 (1) ◽  
pp. 14
Author(s):  
Hendro Supratikno ◽  
David Premana
Keyword(s):  
Systematic Error ◽  
Error Propagation ◽  
Measurement Data ◽  
Systematic Errors ◽  
Random Errors ◽  
Land Area ◽  
Parking Lots ◽  
Parking Lot ◽  
Data Error ◽  
Definition Of

Parking is a condition of not moving a vehicle that is temporary because it was abandoned by the driver. Included in the definition of parking is every vehicle that stops at certain places whether stated by traffic signs or not, and not solely for the benefit of raising and / or lowering people and / or goods.Campus 3 Lumajang State Community Academy has facilities and infrastructure prepared by the Lumajang Regency government. However, the parking lots provided cannot accommodate vehicles optimally because of the ratio of the number of vehicles and the area of the parking area that is not appropriate. This is because the area of the parking lot is not analyzed by data error when measuring.Each measurement data is assumed to have errors both systematic errors, random errors, and large errors (blunders), so that in the measurement of parking lots certainly there are errors. From this the authors intend to conduct research to find out how the propagation of systematic errors and the large systematic errors of the area of campus parking lot 3 Lumajang Community Academy.The methods used in this study include preparing materials and tools, making land sketches, decomposing them, determining distances using theodolite, determining land area equations, and finding systematic error propagation. So that the final goal in this study is to find large systematic errors in the parking area of Campus 3 of the Lumajang State Community Academy

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