scholarly journals Tracking the global flows of atmospheric moisture and associated uncertainties

2020 ◽  
Vol 24 (5) ◽  
pp. 2419-2435 ◽  
Author(s):  
Obbe A. Tuinenburg ◽  
Arie Staal
Keyword(s):  
Vertical Mixing ◽  
Three Dimensional ◽  
Reanalysis Data ◽  
Point Sources ◽  
Atmospheric Moisture ◽  
Data Set ◽  
Considerable Uncertainty ◽  
Moisture Recycling ◽  
Trade Offs ◽  
Spatial Dimensions

Abstract. Many processes in hydrology and Earth system science relate to continental moisture recycling, the contribution of terrestrial evaporation to precipitation. For example, the effects of land-cover changes on regional rainfall regimes depend on this process. To study moisture recycling, a range of moisture-tracking models are in use that are forced with output from atmospheric models but differ in various ways. They can be Eulerian (grid-based) or Lagrangian (trajectory-based), have two or three spatial dimensions, and rely on a range of other assumptions. Which model is most suitable depends not only on the purpose of the study but also on the quality and resolution of the data with which it is forced. Recently, the high-resolution ERA5 reanalysis data set has become the state of the art, paving the way for a new generation of moisture-tracking models. However, it is unclear how the new data can best be used to obtain accurate estimates of atmospheric moisture flows. Here we develop a set of moisture-tracking models forced with ERA5 data and systematically test their performance regarding continental evaporation recycling ratio, distances of moisture flows, and “footprints” of evaporation from seven point sources across the globe. We report simulation times to assess possible trade-offs between accuracy and speed. Three-dimensional Lagrangian models were most accurate and ran faster than Eulerian versions for tracking water from single grid cells. The rate of vertical mixing of moisture in the atmosphere was the greatest source of uncertainty in moisture tracking. We conclude that the recently improved resolution of atmospheric reanalysis data allows for more accurate moisture tracking results in a Lagrangian setting, but that considerable uncertainty regarding turbulent mixing remains. We present an efficient Lagrangian method to track atmospheric moisture flows from any location globally using ERA5 reanalysis data and make the code for this model, which we call UTrack-atmospheric-moisture, publicly available.

scholarly journals Tracking the global flows of atmospheric moisture

2019 ◽  
Author(s):  
Obbe A. Tuinenburg ◽  
Arie Staal
Keyword(s):  
Vertical Mixing ◽  
Three Dimensional ◽  
Reanalysis Data ◽  
Point Sources ◽  
Atmospheric Moisture ◽  
Reanalysis Dataset ◽  
Considerable Uncertainty ◽  
Moisture Recycling ◽  
Trade Offs ◽  
Spatial Dimensions

Abstract. Many processes in hydrology and Earth system science relate to moisture recycling, the contribution of terrestrial evaporation to precipitation. For example, the effects of land-cover changes on regional rainfall regimes depend on this process. To study moisture recycling, a range of moisture tracking models are in use that are forced with output from atmospheric models, but differ in various ways. They can be Eulerian (grid-based) or Lagrangian (trajectory-based), have two or three spatial dimensions, and rely on a range of other assumptions. Which model is most suitable depends on the purpose of the study, but also on the quality and resolution of the data with which it is forced. Recently, the high-resolution ERA5 reanalysis dataset has become the state-of-the-art, paving the way for a new generation of moisture tracking models. However, it is unclear how the new data can best be used to obtain accurate estimates of atmospheric moisture flows. Here we develop a set of moisture tracking models forced with ERA5 data and systematically test their performance regarding continental evaporation recycling ratio, distances of moisture flows, and footprints of evaporation from seven point sources across the globe. We report simulation times to assess possible trade-offs between accuracy and speed. Three-dimensional Lagrangian models were most accurate and ran faster than Eulerian versions for tracking water from single grid cells. The rate of vertical mixing of moisture in the atmosphere was the greatest source of uncertainty in moisture tracking. We conclude that the recently improved resolution of atmospheric reanalysis data allows for more accurate moisture tracking results in a Lagrangian setting, but that considerable uncertainty regarding turbulent mixing remains. We present an efficient Lagrangian method to track atmospheric moisture flows from any location globally using ERA5 reanalysis data and make the code for this model publicly available.

Tracking the global flows of atmospheric moisture

2020 ◽  
Author(s):  
Obbe Tuinenburg ◽  
Arie Staal
Keyword(s):  
Vertical Mixing ◽  
Three Dimensional ◽  
Reanalysis Data ◽  
Point Sources ◽  
Atmospheric Moisture ◽  
Reanalysis Dataset ◽  
Considerable Uncertainty ◽  
Moisture Recycling ◽  
Trade Offs ◽  
Spatial Dimensions

<p>Many processes in hydrology and Earth system science relate to moisture recycling, the contribution of terrestrial evaporation to precipitation. For example, the effects of land-cover changes on regional rainfall regimes depend on this process. To study moisture recycling, a range of moisture tracking models are in use that are forced with output from atmospheric models, but differ in various ways. They can be Eulerian (grid-based) or Lagrangian (trajectory-based), have two or three spatial dimensions, and rely on a range of other assumptions. Which model is most suitable depends on the purpose of the study, but also on the quality and resolution of the data with which it is forced. Recently, the high-resolution ERA5 reanalysis dataset has become the state-of-the-art, paving the way for a new generation of moisture tracking models. However, it is unclear how the new data can best be used to obtain accurate estimates of atmospheric moisture flows. Here we develop a set of moisture tracking models forced with ERA5 data and systematically test their performance regarding continental evaporation recycling ratio, distances of moisture flows, and <q>footprints</q> of evaporation from seven point sources across the globe. We report simulation times to assess possible trade-offs between accuracy and speed. Three-dimensional Lagrangian models were most accurate and ran faster than Eulerian versions for tracking water from single grid cells. The rate of vertical mixing of moisture in the atmosphere was the greatest source of uncertainty in moisture tracking. We conclude that the recently improved resolution of atmospheric reanalysis data allows for more accurate moisture tracking results in a Lagrangian setting, but that considerable uncertainty regarding turbulent mixing remains. We present an efficient Lagrangian method to track atmospheric moisture flows from any location globally using ERA5 reanalysis data and make the code for this model publicly available.</p>

scholarly journals High-resolution global atmospheric moisture connections from evaporation to precipitation

2020 ◽  
Author(s):  
Obbe A. Tuinenburg ◽  
Jolanda J. E. Theeuwen ◽  
Arie Staal
Keyword(s):  
Amazon Basin ◽  
Reanalysis Data ◽  
Congo Basin ◽  
Atmospheric Moisture ◽  
Moisture Recycling ◽  
Wind Speeds ◽  
Complete Dataset ◽  
Global Land ◽  
Land Recycling ◽  
Tracking Model

Abstract. A key Earth system process is the circulation of evaporated moisture through the atmosphere. Spatial connections between evaporation and precipitation affect the global and regional climates by redistributing water and latent heat. Through this atmospheric moisture recycling, land-cover changes influence regional precipitation patterns, with potentially far-reaching effects on human livelihoods and biome distributions across the globe. However, a globally complete dataset of atmospheric moisture flows from evaporation to precipitation has been lacking so far. Here we present a dataset of global atmospheric moisture recycling on both 0.5° and 1.0° spatial resolution. We simulated the moisture flows between each pair of cells across all land and oceans for 2008–2017 and present their monthly climatological means. We applied the Lagrangian moisture tracking model UTrack, which is forced with ERA5 reanalysis data on 25 atmospheric layers and hourly wind speeds and directions. Due to the global coverage of the simulations, a complete picture of both the upwind source areas of precipitation and downwind target areas of evaporation can be obtained. We show a number of statistics of global atmospheric moisture flows: land recycling, basin recycling, mean latitudinal and longitudinal flows, absolute latitudinal and longitudinal flows, and basin recycling for the 26 largest river basins. We find that, on average, 70 % of global land evaporation rains down over land, varying between 62 % and 74 % across the year; 51 % of global land precipitation has evaporated from land, varying between 36 % and 57 % across the year. Highest basin recycling occurs in the Amazon and Congo basins, with evaporation and precipitation recycling of 63 % and 36 % for the Amazon basin and 60 % and 47 % for the Congo basin. These statistics are examples of the potential usage of the dataset, which allows users to identify and quantify the moisture flows from and to any area on Earth, from local to global scales. The dataset is available at https://doi.pangaea.de/10.1594/PANGAEA.912710 (Tuinenburg et al., 2020).

scholarly journals High-resolution global atmospheric moisture connections from evaporation to precipitation

2020 ◽  
Vol 12 (4) ◽  
pp. 3177-3188
Author(s):  
Obbe A. Tuinenburg ◽  
Jolanda J. E. Theeuwen ◽  
Arie Staal
Keyword(s):  
Amazon Basin ◽  
Reanalysis Data ◽  
Congo Basin ◽  
Atmospheric Moisture ◽  
Moisture Recycling ◽  
Wind Speeds ◽  
Complete Dataset ◽  
Global Land ◽  
Land Recycling ◽  
Tracking Model

Abstract. A key Earth system process is the circulation of evaporated moisture through the atmosphere. Spatial connections between evaporation and precipitation affect the global and regional climates by redistributing water and latent heat. Through this atmospheric moisture recycling, land cover changes influence regional precipitation patterns, with potentially far-reaching effects on human livelihoods and biome distributions across the globe. However, a globally complete dataset of atmospheric moisture flows from evaporation to precipitation has been lacking so far. Here we present a dataset of global atmospheric moisture recycling on both 0.5∘ and 1.0∘ spatial resolution. We simulated the moisture flows between each pair of cells across all land and oceans for 2008–2017 and present their monthly climatological means. We applied the Lagrangian moisture tracking model UTrack, which is forced with ERA5 reanalysis data on 25 atmospheric layers and hourly wind speeds and directions. Due to the global coverage of the simulations, a complete picture of both the upwind source areas of precipitation and downwind target areas of evaporation can be obtained. We show a number of statistics of global atmospheric moisture flows: land recycling, basin recycling, mean latitudinal and longitudinal flows, absolute latitudinal and longitudinal flows, and basin recycling for the 26 largest river basins. We find that, on average, 70 % of global land evaporation rains down over land, varying between 62 % and 74 % across the year; 51 % of global land precipitation has evaporated from land, varying between 36 % and 57 % across the year. The highest basin recycling occurs in the Amazon and Congo basins, with evaporation and precipitation recycling of 63 % and 36 % for the Amazon basin and 60 % and 47 % for the Congo basin. These statistics are examples of the potential usage of the dataset, which allows users to identify and quantify the moisture flows from and to any area on Earth, from local to global scales. The dataset is available at https://doi.org/10.1594/PANGAEA.912710 (Tuinenburg et al., 2020).

scholarly journals Zonal-mean data set of global atmospheric reanalyses on pressure levels

2018 ◽  
Author(s):  
Patrick Martineau ◽  
Jonathon S. Wright ◽  
Nuanliang Zhu ◽  
Masatomo Fujiwara
Keyword(s):  
Three Dimensional ◽  
Short Wave ◽  
Reanalysis Data ◽  
Environmental Data ◽  
Data Sets ◽  
Heating Rates ◽  
Data Set ◽  
Intercomparison Project ◽  
Complete Set ◽  
Transformed Eulerian Mean

Abstract. This data set, which is prepared for the SPARC-Reanalysis Intercomparison Project (S-RIP), provides several zonal-mean diagnostics computed from reanalysis data on pressure levels. Diagnostics are currently provided for a variety of reanalyses, including ERA-40, ERA-Interim, ERA-20C, NCEP-NCAR, NCEP-DOE, CFSR, 20CR v2 and v2c, JRA-25, JRA-55, JRA-55C, JRA-55AMIP, MERRA, and MERRA-2. The data set will be expanded to include additional reanalyses as they become available. Basic dynamical variables (such as temperature, geopotential height and three-dimensional winds) are provided in addition to a complete set of terms from the Eulerian-mean and transformed Eulerian-mean momentum equations. Total diabatic heating and its long-wave and short-wave components are included as availability permits, along with heating rates diagnosed from the basic dynamical variables using the zonal-mean thermodynamic equation. Two versions of the data set are provided, one that uses horizontal and vertical grids provided by the various reanalysis centers, and another that uses a common grid to facilitate comparison among data sets. For the common grid, all diagnostics are interpolated horizontally onto a regular 2.5° ×2.5° grid for a subset of pressure levels that are common amongst all included reanalyses. The dynamical (Martineau, 2017, http://dx.doi.org/10.5285/b241a7f536a244749662360bd7839312) and diabatic (Wright, 2017, http://dx.doi.org/10.5285/70146c789eda4296a3c3ab6706931d56) variables are archived and maintained by the Centre for Environmental Data Analysis (CEDA).

scholarly journals Length and time scales of atmospheric moisture recycling

2011 ◽  
Vol 11 (5) ◽  
pp. 1853-1863 ◽  
Author(s):  
R. J. van der Ent ◽  
H. H. G. Savenije
Keyword(s):  
Time Scales ◽  
Scaling Laws ◽  
Reanalysis Data ◽  
Length Scale ◽  
Atmospheric Moisture ◽  
Climatic Effects ◽  
Study Region ◽  
Moisture Recycling ◽  
Shape Dependence ◽  
Novel Approach

Abstract. It is difficult to quantify the degree to which terrestrial evaporation supports the occurrence of precipitation within a certain study region (i.e. regional moisture recycling) due to the scale- and shape-dependence of regional moisture recycling ratios. In this paper we present a novel approach to quantify the spatial and temporal scale of moisture recycling, independent of the size and shape of the region under study. In contrast to previous studies, which essentially used curve fitting, the scaling laws presented by us follow directly from the process equation. thus allowing a fair comparison between regions and seasons. The calculation is based on ERA-Interim reanalysis data for the period 1999 to 2008. It is shown that in the tropics or in mountainous terrain the length scale of recycling can be as low as 500 to 2000 km. In temperate climates the length scale is typically between 3000 to 5000 km whereas it amounts to more than 7000 km in desert areas. The time scale of recycling ranges from 3 to 20 days, with the exception of deserts, where it is much longer. The most distinct seasonal differences can be observed over the Northern Hemisphere: in winter, moisture recycling is insignificant, whereas in summer it plays a major role in the climate. The length and time scales of atmospheric moisture recycling can be useful metrics to quantify local climatic effects of land use change.

scholarly journals Length and time scales of atmospheric moisture recycling

2010 ◽  
Vol 10 (9) ◽  
pp. 21867-21893 ◽  
Author(s):  
R. J. van der Ent ◽  
H. H. G. Savenije
Keyword(s):  
Time Scales ◽  
Reanalysis Data ◽  
Length Scale ◽  
Atmospheric Moisture ◽  
Climatic Effects ◽  
Study Region ◽  
Moisture Recycling ◽  
Shape Dependence ◽  
Novel Approach ◽  
The Tropics

Abstract. It is difficult to quantify the degree to which terrestrial evaporation supports the occurrence of precipitation within a certain study region (i.e. regional moisture recycling) due to the scale- and shape-dependence of regional moisture recycling ratios. In this paper we present a novel approach to quantify the spatial and temporal scale of moisture recycling, independent of the size and shape of the region under study. The calculation is based on ERA-Interim reanalysis data for the period 1999 to 2008. It is shown that in the tropics or in mountainous terrain the length scale of recycling can be as low as 500 to 2000 km. In temperate climates the length scale is typically between 3000 to 5000 km whereas it amounts to more than 7000 km in desert areas. The time scale of recycling ranges from 3 to 20 days, with the exception of deserts, where it is much higher. The most distinct seasonal differences can be observed over the Northern Hemisphere: in winter, moisture recycling is insignificant, whereas in summer it plays a major role in the climate. The length and time scales of atmospheric moisture recycling can be useful metrics to quantify local climatic effects of land use change.

scholarly journals Zonal-mean data set of global atmospheric reanalyses on pressure levels

2018 ◽  
Vol 10 (4) ◽  
pp. 1925-1941 ◽  
Author(s):  
Patrick Martineau ◽  
Jonathon S. Wright ◽  
Nuanliang Zhu ◽  
Masatomo Fujiwara
Keyword(s):  
Three Dimensional ◽  
Reanalysis Data ◽  
Environmental Data ◽  
Data Sets ◽  
Heating Rates ◽  
Data Set ◽  
Intercomparison Project ◽  
Complete Set ◽  
The Common ◽  
Transformed Eulerian Mean

Abstract. This data set, which is prepared for the Stratosphere–troposphere Processes And their Role in Climate (SPARC) Reanalysis Intercomparison Project (S-RIP), provides several zonal-mean diagnostics computed from reanalysis data on pressure levels. Diagnostics are currently provided for a variety of reanalyses, including ERA-40, ERA-Interim, ERA-20C, NCEP–NCAR, NCEP–DOE, CFSR, 20CR v2 and v2c, JRA-25, JRA-55, JRA-55C, JRA-55AMIP, MERRA, and MERRA-2. The data set will be expanded to include additional reanalyses as they become available. Basic dynamical variables (such as temperature, geopotential height, and three-dimensional winds) are provided in addition to a complete set of terms from the Eulerian-mean and transformed-Eulerian-mean momentum equations. Total diabatic heating and its long-wave and shortwave components are included as availability permits, along with heating rates diagnosed from the basic dynamical variables using the zonal-mean thermodynamic equation. Two versions of the data set are provided, one that uses horizontal and vertical grids provided by the various reanalysis centers and another that uses a common grid (CG) to facilitate comparison among data sets. For the common grid, all diagnostics are interpolated horizontally onto a regular 2.5∘×2.5∘ grid for a subset of pressure levels that are common among all included reanalyses. The dynamical (Martineau, 2017, https://doi.org/10.5285/b241a7f536a244749662360bd7839312) and diabatic (Wright, 2017, https://doi.org/10.5285/70146c789eda4296a3c3ab6706931d56) variables are archived and maintained by the Centre for Environmental Data Analysis (CEDA).

Three-dimensional image analysis and visualization in confocal light microscopy

1993 ◽  
Vol 51 ◽  
pp. 158-159
Author(s):  
J. K. Samarabandu ◽  
R. Acharya ◽  
D. R. Pareddy ◽  
P. C. Cheng
Keyword(s):  
Depth Perception ◽  
Computer Graphic ◽  
Three Dimensional ◽  
Cellular Organization ◽  
Data Set ◽  
Computational Burden ◽  
Interactive Operation ◽  
Cell Organization ◽  
Confocal Images ◽  
3D Digitization

In the study of cell organization in a maize meristem, direct viewing of confocal optical sections in 3D (by means of 3D projection of the volumetric data set, Figure 1) becomes very difficult and confusing because of the large number of nucleus involved. Numerical description of the cellular organization (e.g. position, size and orientation of each structure) and computer graphic presentation are some of the solutions to effectively study the structure of such a complex system. An attempt at data-reduction by means of manually contouring cell nucleus in 3D was reported (Summers et al., 1990). Apart from being labour intensive, this 3D digitization technique suffers from the inaccuracies of manual 3D tracing related to the depth perception of the operator. However, it does demonstrate that reducing stack of confocal images to a 3D graphic representation helps to visualize and analyze complex tissues (Figure 2). This procedure also significantly reduce computational burden in an interactive operation.

EMCAT: An automatic image-processing system for electron-microscopic tomography

1994 ◽  
Vol 52 ◽  
pp. 418-419
Author(s):  
Weiping Liu ◽  
John W. Sedat ◽  
David A. Agard
Keyword(s):  
Image Processing ◽  
Structural Information ◽  
Imaging Technique ◽  
Three Dimensional ◽  
Processing System ◽  
Electron Microscopic ◽  
Data Set ◽  
Ordered Structures ◽  
Automatic Image Processing ◽  
Em Tomography

Any real world object is three-dimensional. The principle of tomography, which reconstructs the 3-D structure of an object from its 2-D projections of different view angles has found application in many disciplines. Electron Microscopic (EM) tomography on non-ordered structures (e.g., subcellular structures in biology and non-crystalline structures in material science) has been exercised sporadically in the last twenty years or so. As vital as is the 3-D structural information and with no existing alternative 3-D imaging technique to compete in its high resolution range, the technique to date remains the kingdom of a brave few. Its tedious tasks have been preventing it from being a routine tool. One keyword in promoting its popularity is automation: The data collection has been automated in our lab, which can routinely yield a data set of over 100 projections in the matter of a few hours. Now the image processing part is also automated. Such automations finish the job easier, faster and better.

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