Phase Doppler quantification of agricultural spray compared with traditional sampling materials

The quantification of spray mass has historically been accomplished by means of fluorescent dyes and various string and ground samplers to capture the dye-laden spray. However, these methods are typically not used in close proximity to orchard sprayers and are prone to many sources of error. The objective of this study was to assess the ability of an in-field phase Doppler (pD) interferometer to quantify spray mass against two common string samplers. Measurements were taken at 0.5 m increments to 4.5 m vertically and 1.0 m increments to 5.0 m downwind from the spray. Converted flux measurements from the strings were compared with those obtained using the pD interferometer. The current pD technology was found to be incapable of collecting equivalent flux data to that obtained from the strings. However, the pD equipment did provide useful data on droplet velocity and size.

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Close-range radar rainfall estimation and error analysis

Atmospheric Measurement Techniques ◽

10.5194/amt-9-3837-2016 ◽

2016 ◽

Vol 9 (8) ◽

pp. 3837-3850 ◽

Cited By ~ 9

Author(s):

C. Z. van de Beek ◽

H. Leijnse ◽

P. Hazenberg ◽

R. Uijlenhoet

Keyword(s):

Positive Impact ◽

Rain Gauge ◽

Minor Role ◽

Rainfall Estimation ◽

Radar Rainfall ◽

Sources Of Error ◽

Ground Clutter ◽

Received Power ◽

Radar Calibration ◽

Many Sources

Abstract. Quantitative precipitation estimation (QPE) using ground-based weather radar is affected by many sources of error. The most important of these are (1) radar calibration, (2) ground clutter, (3) wet-radome attenuation, (4) rain-induced attenuation, (5) vertical variability in rain drop size distribution (DSD), (6) non-uniform beam filling and (7) variations in DSD. This study presents an attempt to separate and quantify these sources of error in flat terrain very close to the radar (1–2 km), where (4), (5) and (6) only play a minor role. Other important errors exist, like beam blockage, WLAN interferences and hail contamination and are briefly mentioned, but not considered in the analysis. A 3-day rainfall event (25–27 August 2010) that produced more than 50 mm of precipitation in De Bilt, the Netherlands, is analyzed using radar, rain gauge and disdrometer data. Without any correction, it is found that the radar severely underestimates the total rain amount (by more than 50 %). The calibration of the radar receiver is operationally monitored by analyzing the received power from the sun. This turns out to cause a 1 dB underestimation. The operational clutter filter applied by KNMI is found to incorrectly identify precipitation as clutter, especially at near-zero Doppler velocities. An alternative simple clutter removal scheme using a clear sky clutter map improves the rainfall estimation slightly. To investigate the effect of wet-radome attenuation, stable returns from buildings close to the radar are analyzed. It is shown that this may have caused an underestimation of up to 4 dB. Finally, a disdrometer is used to derive event and intra-event specific Z–R relations due to variations in the observed DSDs. Such variations may result in errors when applying the operational Marshall–Palmer Z–R relation. Correcting for all of these effects has a large positive impact on the radar-derived precipitation estimates and yields a good match between radar QPE and gauge measurements, with a difference of 5–8 %. This shows the potential of radar as a tool for rainfall estimation, especially at close ranges, but also underlines the importance of applying radar correction methods as individual errors can have a large detrimental impact on the QPE performance of the radar.

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Methodological errors in radioisotope flux measurements

AJP Gastrointestinal and Liver Physiology ◽

10.1152/ajpgi.1988.255.5.g696 ◽

1988 ◽

Vol 255 (5) ◽

pp. G696-G699

Author(s):

R. W. Egnor ◽

S. G. Vaccarezza ◽

A. N. Charney

Keyword(s):

Specific Activity ◽

Flux Measurement ◽

Sampling Interval ◽

Flux Chamber ◽

Sources Of Error ◽

Counting Time ◽

Flux Measurements ◽

The Absolute ◽

The Difference ◽

Serial Samples

We examined several sources of error in isotopic flux measurements in a commonly used experimental model: the study of 22Na and 36Cl fluxes across rat ileal tissue mounted in the Ussing flux chamber. The experiment revealed three important sources of error: the absolute counts per minute, the difference in counts per minute between serial samples, and averaging of serial samples. By computer manipulation, we then applied hypothetical changes in the experimental protocol to generalize these findings and assess the effect and interaction of the absolute counts per minute, the sampling interval, and the counting time on the magnitude of the error. We found that the error of a flux measurement will vary inversely with the counting time and the difference between the consecutive sample counts per minute used in the flux calculations and will vary directly with the absolute counts per minute of each sample. Alteration of the "hot" side specific activity, the surface area of the tissue across which flux is measured and the sample volume have a smaller impact on measurement error. Experimental protocols should be designed with these methodological considerations in mind to minimize the error inherent in measuring isotope flux.

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Cadastral plotting by similarity transformation

CISM journal ◽

10.1139/geomat-1988-0011 ◽

1988 ◽

Vol 42 (2) ◽

pp. 121-125 ◽

Cited By ~ 2

Author(s):

Roy Gagnon

Keyword(s):

Numerical Methods ◽

Complex Number ◽

Similarity Transformation ◽

Cost Effective ◽

Human Interface ◽

Sources Of Error ◽

Cadastral Maps ◽

Numerical Transformation ◽

Digital Methods ◽

Many Sources

Cadastral maps generated from digitizing tabloids have too many sources of error to be reliable when plotted at large scales. To improve the 1:1000 cadastral map, three numerical methods were tested during twenty-seven mapping projects. The systems studied were: complex number similarity transformation, simultaneous similarity transformation, and iterative similarity transformation. There were several “human interface” difficulties with the computer routines but all of the programs were found to be reliable, and cost effective. All of the resulting cadastral maps are in daily use. Numerical transformation was found superior to digital methods of cadastral compilation.

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University of Michigan Radiocarbon Dates XI

Radiocarbon ◽

10.1017/s0033822200000163 ◽

1966 ◽

Vol 8 ◽

pp. 256-285 ◽

Cited By ~ 13

Author(s):

H. R. Crane ◽

James B. Griffin

Keyword(s):

Standard Deviation ◽

Half Life ◽

Radiocarbon Dating ◽

Geiger Counter ◽

University Of Michigan ◽

Radiocarbon Dates ◽

Statistical Errors ◽

Sources Of Error ◽

The Many ◽

Many Sources

The following is a list of dates obtained since the time of the compilation of List X in December 1964. The method is essentially the same as that used for the work described in the previous list. Two CO2-CS2Geiger counter systems are used. The equipment and counting techniques have been described elsewhere (Crane, 1961). The dates and estimates of error in this list follow the practice recommended by the International Radiocarbon Dating Conferences of 1962 and 1965, in that (a) dates are computed on the basis of the Libby half-life, 5570 yr, (b) A.D. 1950 is used as the zero of the age scale, and (c) the errors quoted are the standard deviations obtained from the numbers of counts only. In previous Michigan date lists up to and including VII we have quoted errors at least twice as great as the statistical errors of counting, in order to take account of other errors in the over-all process. If the reader wishes to obtain a standard deviation figure which will allow ample room for the many sources of error in the dating process, we suggest he double the figures that are given in this list.

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XIII.—The Strains Produced in Iron, Steel, and Nickel Tubes in the Magnetic Field. Part I.

Transactions of the Royal Society of Edinburgh ◽

10.1017/s0080456800033378 ◽

1897 ◽

Vol 38 (3) ◽

pp. 527-555 ◽

Cited By ~ 1

Author(s):

C. G. Knott

Keyword(s):

Magnetic Field ◽

Short Note ◽

Interesting Phenomenon ◽

Senior Student ◽

Sources Of Error ◽

Physical Laboratory ◽

The Many ◽

Many Sources ◽

The Magnetic Field ◽

Interior Volume

On July 20th, 1891, I communicated to the Society a short note on the effect of longitudinal magnetisation on the interior volume of iron and nickel tubes (see Proceedings, 1890–91, pp. 315–7). These earliest results of observation of a new and interesting phenomenon in magnetic strains were obtained during my last few months' residence in Japan. In following out the lines of research therein suggested, I have been fortunate in having had placed at my disposal by Professor Tait the resources of the Physical Laboratory of Edinburgh University. I desire here to record my great indebtedness to him for the interest he has taken in the work, and for his many helpful suggestions. In surmounting the many experimental difficulties met with at every turn, I had the invaluable co-operation of Mr A. Shand, a senior student in the Physical Laboratory. Various results obtained since 1892 have been communicated in short notes from time to time (see Proceedings, 1891–2, pp. 85–88, 249–252; 1893–4, pp. 295–7; 1894–5, pp. 334–5; see also B. A. Reports, 1892 and 1893); but it was not possible to regard these as altogether satisfactory. It was only in May of last year (1895) that the many sources of error were finally got rid of, and the apparatus perfected. The present paper deals entirely with the results obtained since then. In these later experiments I was ably assisted by Mr A. C. Smith, a student in the Physical Laboratory.

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Close-range radar rainfall estimation and error analysis

10.5194/amt-2016-77 ◽

2016 ◽

Author(s):

Remco van de Beek ◽

Hidde Leijnse ◽

Pieter Hazenberg ◽

Remko Uijlenhoet

Keyword(s):

Positive Impact ◽

Rain Gauge ◽

Minor Role ◽

Rainfall Estimation ◽

Radar Rainfall ◽

Sources Of Error ◽

Ground Clutter ◽

Received Power ◽

Radar Calibration ◽

Many Sources

Abstract. Quantitative precipitation estimation (QPE) using ground-based weather radar is affected by many sources of error. The most important of these are 1) radar calibration, 2) ground clutter, 3) wet radome attenuation, 4) rain induced attenuation, 5) vertical profile of reflectivity, 6) non-uniform beam filling, and 7) variations in rain drop size distribution (DSD). This study presents an attempt to separate and quantify these sources of error in flat terrain very close to the radar (1–2 km), where 4), 5), and 6) only play a minor role. A 3-day rainfall event (25–27 August 2010) that produced more than 50 mm of precipitation in De Bilt, The Netherlands is analyzed using radar, rain gauge, and disdrometer data. Without any correction it is found that the radar severely underestimates the total rain amount (by more than 50 %). The calibration of the radar receiver is operationally monitored by analyzing the received power from the sun. This turns out to cause a 1 dB of underestimation. The operational clutter filter applied by KNMI is found to incorrectly identify precipitation as clutter, especially at near-zero Doppler velocities. An alternative simple clutter removal scheme using a clear sky clutter map improves the rainfall estimation slightly. To investigate the effect of wet radome attenuation, stable returns from buildings close to the radar are analyzed. It is shown that this may have caused an underestimation of up to 4 dB. Finally, a disdrometer is used to derive event and intra-event specific Z-R relations due to variations in the observed DSDs. Such variations may result in errors when applying the operational Marshall-Palmer Z-R relation. Correcting for all of these effects has a large positive impact on the radar derived precipitation estimates and yields a good match between radar QPE and gauge measurements, with a difference of 5 to 8 %. This shows the potential of radar as a tool for rainfall estimation, especially at close ranges, but also underlines the importance of applying radar correction methods as individual errors can have a large detrimental impact on the QPE performance of the radar.

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Towards standardized processing of eddy covariance flux measurements of carbonyl sulfide

10.5194/amt-2019-313 ◽

2019 ◽

Author(s):

Kukka-Maaria Kohonen ◽

Pasi Kolari ◽

Linda M. J. Kooijmans ◽

Huilin Chen ◽

Ulli Seibt ◽

...

Keyword(s):

Data Processing ◽

Eddy Covariance ◽

Signal To Noise Ratio ◽

Carbonyl Sulfide ◽

Lag Time ◽

Maximum Effect ◽

Signal To Noise ◽

Flux Data ◽

Flux Measurements ◽

Noise Ratio

Abstract. Carbonyl sulfide (COS) flux measurements with the eddy covariance (EC) technique are growing in popularity with the recent development in using COS to estimate gross photosynthesis at the ecosystem scale. Flux data intercomparison would benefit from standardized protocols for COS flux data processing. In this study, we analyze how various data processing steps affect the final flux and provide a method for gap-filling COS fluxes. Different methods for determining the lag time between COS mixing ratio and the vertical wind velocity (w) resulted in a maximum of 12 % difference in the cumulative COS flux. Due to limited COS measurement precision, small COS fluxes (below approximately 3 pmol m−2 s−1) could not be detected when the lag time was determined from maximizing the covariance between COS and w. We recommend using a combination of COS and carbon dioxide (CO2) lag times in determining the COS flux, depending on the flux magnitude compared to the detection limit of each averaging period. Different high frequency spectral corrections had a maximum effect of 10 % on COS flux calculations and different detrending methods only 1.2 %. Relative total uncertainty was more than five times higher for low COS fluxes (absolute flux lower than 3 pmol m−2 s−1) than for low CO2 fluxes (lower than 1.5 μmol m−2 s−1), indicating a low signal-to-noise ratio of COS fluxes. Due to similarities in ecosystem COS and CO2 exchange, and the low signal-to-noise ratio of COS fluxes that is similar to methane, we recommend a combination of CO2 and methane flux processing protocols for COS EC fluxes.

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Gap-filling strategies for annual VOC flux data sets

Biogeosciences ◽

10.5194/bg-11-2429-2014 ◽

2014 ◽

Vol 11 (8) ◽

pp. 2429-2442 ◽

Cited By ~ 10

Author(s):

I. Bamberger ◽

L. Hörtnagl ◽

M. Walser ◽

A. Hansel ◽

G. Wohlfahrt

Keyword(s):

Linear Interpolation ◽

Winter Period ◽

Data Sets ◽

Gap Filling ◽

Data Set ◽

Mountain Meadow ◽

Flux Data ◽

Flux Measurements ◽

Annual Patterns

Abstract. Up to now the limited knowledge about the exchange of volatile organic compounds (VOCs) between the biosphere and the atmosphere is one of the factors which hinders more accurate climate predictions. Complete long-term flux data sets of several VOCs to quantify the annual exchange and validate recent VOC models are basically not available. In combination with long-term VOC flux measurements the application of gap-filling routines is inevitable in order to replace missing data and make an important step towards a better understanding of the VOC ecosystem–atmosphere exchange on longer timescales. We performed VOC flux measurements above a mountain meadow in Austria during two complete growing seasons (from snowmelt in spring to snow reestablishment in late autumn) and used this data set to test the performance of four different gap-filling routines, mean diurnal variation (MDV), mean gliding window (MGW), look-up tables (LUT) and linear interpolation (LIP), in terms of their ability to replace missing flux data in order to obtain reliable VOC sums. According to our findings the MDV routine was outstanding with regard to the minimization of the gap-filling error for both years and all quantified VOCs. The other gap-filling routines, which performed gap-filling on 24 h average values, introduced considerably larger uncertainties. The error which was introduced by the application of the different filling routines increased linearly with the number of data gaps. Although average VOC fluxes measured during the winter period (complete snow coverage) were close to zero, these were highly variable and the filling of the winter period resulted in considerably higher uncertainties compared to the application of gap-filling during the measurement period. The annual patterns of the overall cumulative fluxes for the quantified VOCs showed a completely different behaviour in 2009, which was an exceptional year due to the occurrence of a severe hailstorm, compared to 2011. Methanol was the compound which, at 381.5 mg C m−2 and 449.9 mg C m−2, contributed most to the cumulative VOC carbon emissions in 2009 and 2011, respectively. In contrast to methanol emissions, however, considerable amounts of monoterpenes (−327.3 mg C m−2) were deposited onto the mountain meadow during 2009 caused by a hailstorm. Other quantified VOCs had considerably lower influences on the annual patterns.

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University of Michigan Radiocarbon Dates XII

Radiocarbon ◽

10.1017/s0033822200019937 ◽

1968 ◽

Vol 10 (1) ◽

pp. 61-114 ◽

Cited By ~ 29

Author(s):

H. R. Crane ◽

James B. Griffin

Keyword(s):

Standard Deviation ◽

Half Life ◽

Radiocarbon Dating ◽

Geiger Counter ◽

University Of Michigan ◽

Radiocarbon Dates ◽

Statistical Errors ◽

Sources Of Error ◽

The Many ◽

Many Sources

The following is a list of dates obtained since the compilation of List XI in December 1965. The method is essentially the same as described in that list. Two CO2-CS2Geiger counter systems were used. Equipment and counting techniques have been described elsewhere (Crane, 1961). Dates and estimates of error in this list follow the practice recommended by the International Radiocarbon Dating Conferences of 1962 and 1965, in that (a) dates are computed on the basis of the Libby half-life, 5570 yr, (b) A.D. 1950 is used as the zero of the age scale, and (c) the errors quoted are the standard deviations obtained from the numbers of counts only. In previous Michigan date lists up to and including VII, we have quoted errors at least twice as great as the statistical errors of counting, to take account of other errors in the over-all process. If the reader wishes to obtain a standard deviation figure which will allow ample room for the many sources of error in the dating process, we suggest doubling the figures that are given in this list.We wish to acknowledge the help of Patricia Dahlstrom in preparing chemical samples and David M. Griffin and Linda B. Halsey in preparing the descriptions.

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Experimental mass-flux measurements: a comparison of different gauges with estimated theoretical data

Annals of Glaciology ◽

10.1017/s0260305500014865 ◽

1998 ◽

Vol 26 ◽

pp. 225-230 ◽

Cited By ~ 4

Author(s):

D. Font ◽

M. Mases ◽

J.M. Vilaplana

Keyword(s):

Mass Flux ◽

Field Data ◽

Theoretical Data ◽

Trapping Efficiency ◽

Experimental Plot ◽

Experimental Site ◽

Ski Resort ◽

Flux Data ◽

Spanish Pyrenees ◽

Flux Measurements

In experimental snowdrifting mass-flux measurements many different instruments have been tested (Takeuchi, CEMAGREE Mases, etc.). Very often the results obtained are a function of the gauge used. However, in order to compare data from different instruments, orders of magnitude have to be similar.Since 1992, snowdrifting has been studied at an experimental plot at La Molina ski resort (eastern Spanish Pyrenees). The alpine site, characterized by a plateau topography, is located at 2250 m.In this paper, different gauges used to measure snowdrifting mass flux at this site are presented: one snow-collector column and two types of snow traps. Snow-collector columns (prismatic boxes) are permanent installations and are used to measure the mass-flux episode. Snow traps (Takeuchi, 1980: modified) are lighter and more mobile, and they are used for short experiments during a wind episode during which mass-flux data are obtained.The three different gauges are compared and the rate of trapping efficiency is suggested from a comparison of the field data with estimated mass-flux data deduced from empirical formulae (Mellor and Fellers, 1986; Naaim-Bouvet and others, 1996). The mass-flux values obtained at the experimental site are lower than the estimated values.

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