Migration Property List
Get a list of MigrationProperty objects.
GET /api/v3/migrationproperties/?format=api&offset=5500
{ "count": 5522, "next": null, "previous": "https://api.catalogue.ceda.ac.uk/api/v3/migrationproperties/?format=api&limit=100&offset=5400", "results": [ { "id": 11290, "key": "project.content.extra", "value": " <br />\nThis research will investigate radiative transfer-based methods to interpret hyperspectral imagery and to estimate leaf biochemical and canopy biophysical variables. These are leaf chlorophyll a+b (Ca+b), dry matter (Cm), water (Cw), leaf area index (LAI), canopy fractional cover, crown volume and tree dimensions. AThese variables are required for modelling land surface / atmosphere interactions, and are indicators of stress and growth. The data will be used to develop and test methofd for estimation by inverse radiative transfer modelling from hyperspectral data in the 400-2500 nm spectral region. In addition, LiDAR data, if available, will provide information regarding the canopy structure that is required for input into the physical models for estimation of canopy biophysical variables. \n\nA field sampling campaign in collaboration with IAS, Cordoba, will be conducted for biochemical analysis of leaf chlorophyll content, measuring reflectance and transmittance using a Li-Cor 1800-12. LAI will be measured using a PCA LAI-2000 instrument. Atmospheric measurements will be collected at the time of over-flights for atmospheric correction of images. The research will study i) the simulation of crown and canopy reflectance with the FLIGHT 3-D model; ii) the estimation of crown and canopy structural variables with a LiDAR instrument, if available, such as crown dimensions, tree height and architecture; and iii) validate the link of the PROSPECT leaf model with FLIGHT for estimation of Ca+b and LAI, and canopy fractional cover.", "modified": "2015-01-08", "ob_ref": 12118 }, { "id": 11291, "key": "project.moles2_activity_subtype", "value": "dgActivityDataCampaign", "modified": "2015-01-08", "ob_ref": 12118 }, { "id": 11292, "key": "project.content.extra", "value": " <br />UK river catchments are experiencing increasing development which, in association with climate change, is placing many urban areas at an increased risk from flooding. In order to understand the likely damage and cost of urban flooding, as well as the influence of urban areas on channel discharge, fluvial flood models need to explicitly represent the complex topography and hydraulically important features of urban areas; information difficult to obtain by traditional survey methods. This research will employ an integrated remote sensing approach to derive urban topographic features and hydraulic parameters required for an explicit urban fluvial flood model. LIDAR, digital aerial photography and multispectral image data will be employed in an integrated framework to derive information on surface topography, 3-D features, surface type and surface texture/roughness for model parameterisation. These will be used to model known historical urban flood inundation for the river Ouseburn; for which data on previous flood events is available. The model will also be compared to models that do not include urban topography and features. The output of this research will be a better understanding of how urban areas should be managed in flood models and the utility of remote sensing for the parameterisation of these.", "modified": "2015-01-08", "ob_ref": 12119 }, { "id": 11293, "key": "project.moles2_activity_subtype", "value": "dgActivityDataCampaign", "modified": "2015-01-08", "ob_ref": 12119 }, { "id": 11294, "key": "project.moles2_activity_subtype", "value": "dgActivityDataCampaign", "modified": "2015-01-08", "ob_ref": 12120 }, { "id": 11295, "key": "project.content.extra", "value": " <br />\nThe white ribbon zone is an area marked as 'no data' on geological and marine maps. This zone is never mapped from either land or sea and yet important processes occur in this zone. With a possible rise in sea level due to climate change and an increase in the risk of coastal flooding it is important to characterise the sediments within the integrated coastal zone (ICZ) for coastal management activities. So far only aerial photography has been used to map the coastal zone. Combined analysis of hyperspectral airborne data and field spectrometry data will provide spectral information to determine the composition of deposits within the coastal zone and give more detailed information for costal zone mapping. Grain size will be determined using integrated Lidar data and ground truthing techniques. The study site chosen is a section of coast around the Isle of Wight, chosen because of the large amount of field data that exists for the coastal zone around the Isle of Wight and for the diverse range of sediment types exposed. Techniques will be developed to characterise these coastal sediments remotely both in an out of the shallow marine zone.", "modified": "2015-01-08", "ob_ref": 12121 }, { "id": 11296, "key": "project.moles2_activity_subtype", "value": "dgActivityDataCampaign", "modified": "2015-01-08", "ob_ref": 12121 }, { "id": 11297, "key": "project.moles2_activity_subtype", "value": "dgActivityDataCampaign", "modified": "2015-01-08", "ob_ref": 12122 }, { "id": 11298, "key": "project.content.extra", "value": " <br />\nThe artificial breaching of existing seawalls - managed realignment - has been implemented to counteract habitat losses by re-creating saltmarsh on formerly reclaimed land. Changes in shoreline position and the re-establishment of tidal exchange are likely to have implications for salt marshes and mudflats in front of, and adjacent to, such newly created intertidal areas. However, the way in which such schemes should be designed for maximum benefit but minimal environmental impact on adjacent coastal ecosystems is still poorly known. Optical remote sensing, and particularly the recording of multi-spectral imagery from aircraft-mounted instruments flown over the coastal zone, offers a rapid, repeatable, non-intrusive and relatively large scale monitoring system for assessing these external impacts. The Wash Banks Flood Defence Scheme is being studied with ATM imagery collected once before breaching (pre-9/2002) and twice afterwards. It has identified the impact of inundation on formerly reclaimed land, the relative stability of the existing saltmarsh surface and the major changes to the depth and distribution of creeks. It is crucial that this data collection is continued to demonstrate the full impacts over a realistic time interval.", "modified": "2015-01-08", "ob_ref": 12123 }, { "id": 11299, "key": "project.moles2_activity_subtype", "value": "dgActivityDataCampaign", "modified": "2015-01-08", "ob_ref": 12123 }, { "id": 11301, "key": "project.moles2_activity_subtype", "value": "dgActivityDataCampaign", "modified": "2015-01-08", "ob_ref": 12124 }, { "id": 11302, "key": "project.content.extra", "value": " <br />Previous studies by the applicants have quantified the applicability of LiDAR data for reach-scale topographic mapping of gravel-bed channel systems. To model interactions between the macro-scale system drivers (hydrology, sediment movement) and the biotic communities on the meso-scale (geomorphological unit, response to individual events) the requirement is for an integrated mapping approach by augmenting LiDAR using appropriately georeferenced ground survey. The Coquet research has as its central focus better determination of the 'properties of patchiness' and how these influence river corridor processes and habitat availability and biodiversity. Earlier approaches have focused on attributes such as patch geometry and sediment accrual, but have ignored the influence of this on habitat change. Research objectives include elucidating temporal aspects of patch formation, persistence and function, determining feedback mechanisms between patchiness, instream processes and biotic diversity and linking hydrological processes with 'ex-channel habitat' including the wider floodplain. A detailed baseline exists for the study area as a result of acquisition of the 1998 LiDAR dataset. Updating the elevation models by a new LiDAR survey, along with ground-truth validation will allow spatial up-scaling from the site to an extended (5.5 km) reach scale, an exercise that will also benefit management approaches for these systems.", "modified": "2015-01-08", "ob_ref": 12125 }, { "id": 11303, "key": "project.moles2_activity_subtype", "value": "dgActivityDataCampaign", "modified": "2015-01-08", "ob_ref": 12125 }, { "id": 11304, "key": "project.moles2_activity_subtype", "value": "dgFundingProgram", "modified": "2015-01-08", "ob_ref": 12126 }, { "id": 11306, "key": "project.moles2_activity_subtype", "value": "dgActivityDataCampaign", "modified": "2015-01-08", "ob_ref": 12127 }, { "id": 11308, "key": "project.moles2_activity_subtype", "value": "dgActivityDataCampaign", "modified": "2015-01-08", "ob_ref": 12128 }, { "id": 11310, "key": "project.moles2_activity_subtype", "value": "dgActivityDataCampaign", "modified": "2015-01-08", "ob_ref": 12129 }, { "id": 11315, "key": "Computation.content.introduction", "value": "<p>MITgcm:</font>\r\n<ul>\r\n <li> can be used to study both <a href=\"\">atmospheric</a> and <a href=\"http://mitgcm.org/public/sealion/online_documents/node7.html\">oceanic</a> circulation</li>\r\n <li> has a <a href=\"http://mitgcm.org/public/sealion/online_documents/node8.html\">non-hydrostatic</a> capability</li>\r\n <li> supports <a href=\"http://mitgcm.org/public/sealion/online_documents/node60.html\">horizontal orthogonal curvilinear coordinates</a></li>\r\n <li> has a <a href=\"http://mitgcm.org/public/sealion/online_documents/node3.html\">finite volume</a> treatment of topography</li>\r\n <li> supports a wide range of <a href=\"http://mitgcm.org/public/sealion/online_documents/node193.html\">physical parameterizations</a>,/li>\r\n <li> as <a href=\"http://mitgcm.org/public/sealion/online_documents/node10.html\">tangent linear and adjoint code</a> maintained alongside the forward model</li>\r\n <li> can run on your pc, workstation or parallel computer using flexible <a href=\"http://mitgcm.org/public/sealion/online_documents/node164.html\"> domain decomposition</a> </li>\r\n\r\n</ul>\r\n\r\nTo render atmosphere and ocean models from one dynamical core MITGCM exploit `isomorphisms' between equation sets that govern the evolution of the respective fluids. One system of hydrodynamical equations is written down and encoded. The model variables have different interpretations depending on whether the atmosphere or ocean is being studied. Thus, for example, the vertical coordinate `r' is interpreted as pressure, `p', in the atmosphere and height, `z', in the ocean. From a numerical implementation point of view there is no fundamental difference between atmosphere and ocean in the MITGCM model.\r\n\r\nTo know more about the way MITGCM exploit `isomorphisms' between atmosphere and ocean governing equations see : <a href=\"../../../paoc/papers/atmosphere_ocean_modeling.pdf\" target=\"_blank\">\r\n\tMarshall, J. A. Adcroft, J-M Campin and C. Hill (2004) Atmosphere-ocean \r\n\tmodeling exploiting fluid isomorphisms.</a>&#160; Mon. Wea. Rev., 132 (12), \r\n\t2882-2894</p>\r\n\r\n<p> The horizontal and vertical representation, resolution and other important characteristics of the <a href=\"http://mitgcm.org/public/docs.html\">\" MITgcm \"</a> \r\n hydrodynamical kernel used for the study of the circulation of atmosphere and ocean are as follows (2011):</p>\r\n\r\n\r\n<p> </p><h2> A. Atmosphere </h2>\r\n<ol>\r\n<li> resolution\r\n\r\n<p> The model has run using different grids. The coarsest resolution grid used was C32 (the cubic grid of Rancic and Purser\r\nwith 32 points across a tile) which is equivalent to G64 (128&#215;64 points in spherical polar coordinates) in equatorial resolution. Other grid resolutions are C46, C64 and C96 all using the conformal cubic grid of Rancic et al. (1996).\r\n<br />More on model grid resolution: <a href=\"http://paoc.mit.edu//paoc/papers/adcroft_et_al_MWR_2004.pdf\" target=\"_blank\">Adcroft,\r\n A., J-M Campin, C. Hill and J. Marshall (2004) Implementation of\r\n an atmosphere-ocean general circulation model on the expanded\r\n spherical cube.</a>&#160; Mon. Wea. Rev., 132 (12), 2845-2863\r\n</p></li>\r\n\r\n<li> numerical scheme/grid \r\n\r\n<p> o Grid - Arakawa C grid. The basic algorithm employed for stepping forward the momentum equations is based on retaining non-divergence of the flow at all times. This is most naturally done if the components of flow are staggered in space in the form of an Arakawa C grid. \r\nThe finite volume method is used to discretize the equations in space.\r\n <br align=\"left\" />More on the finite volume implementation: Adcroft, A.J., Hill, C.N. and J. Marshall, (1997) Representation of topography by shaved cells in a height coordinate ocean model&#160; <em>Mon Wea Rev</em>, vol 125, 2293-2315 \r\n</p><p> o Time-stepping - The algorithm for each of the 5 basic formulations in which the model comes is:\r\n </p><ol>\r\n <li> the semi-implicit pressure method for hydrostatic equations with a rigid-lid, variables co-located in time and with Adams-Bashforth time-stepping,</li> \r\n <li> as 1 but with an implicit linear free-surface,</li> \r\n <li> as 1 or 2 but with variables staggered in time,</li> \r\n <li> as 1 or 2 but with non-hydrostatic terms included,</li> \r\n <li> as 1 or 3 but with non-linear free-surface.</li> \r\n </ol> \r\n<p><br align=\"left\" />More on discretization and time-stepping: <a href=\"http://mitgcm.org/public/r2_manual/latest/online_documents/node30.html\"> \" here \"</a>\r\n\r\n\r\n</p></li><li> list of prognostic variables :\r\n<p> The equations of motion integrated by the model involve four prognostic equations for flow: the two horizontal components of velocity, temperature, potential temperature and salt/moisture, and three diagnostic equations for vertical flow, density/buoyancy, and pressure/geo-potential. \r\n<br /> In addition, the surface pressure or height may by described by either a prognostic or diagnostic equation and if non-hydrostatics terms are included then a diagnostic equation for non-hydrostatic pressure is also solved. </p></li>\r\n\r\n<li> Major atmospheric parameterizations.\r\nThe atmospheric parameterizations are based on the Atmospheric Intermediate Physics aim_v23 package that is based on the version v23 of the SPEEDY code described in: Molteni, F., Atmospheric simulations using a GCM with simplified physical parametrization, I: Model climatology and variability in multidecadal experiments, Clim. Dynamics, 20, 175-191, 2003. The parameters are:\r\n\r\n<p>\r\n</p><pre>------------------------------------------------------------------------\r\n&lt;-Name-&gt;|Levs|&lt;-parsing code-&gt;|&lt;-- Units --&gt;|&lt;- Tile (max=80c) \r\n------------------------------------------------------------------------\r\nDIABT | 5 |SM ML |K/s |Pot. Temp. Tendency (Mass-Weighted) from Diabatic Processes\r\nDIABQ | 5 |SM ML |g/kg/s |Spec.Humid. Tendency (Mass-Weighted) from Diabatic Processes\r\nRADSW | 5 |SM ML |K/s |Temperature Tendency due to Shortwave Radiation (TT_RSW)\r\nRADLW | 5 |SM ML |K/s |Temperature Tendency due to Longwave Radiation (TT_RLW)\r\nDTCONV | 5 |SM MR |K/s |Temperature Tendency due to Convection (TT_CNV)\r\nTURBT | 5 |SM ML |K/s |Temperature Tendency due to Turbulence in PBL (TT_PBL)\r\nDTLS | 5 |SM ML |K/s |Temperature Tendency due to Large-scale condens. (TT_LSC)\r\nDQCONV | 5 |SM MR |g/kg/s |Spec. Humidity Tendency due to Convection (QT_CNV)\r\nTURBQ | 5 |SM ML |g/kg/s |Spec. Humidity Tendency due to Turbulence in PBL (QT_PBL)\r\nDQLS | 5 |SM ML |g/kg/s |Spec. Humidity Tendency due to Large-Scale Condens. (QT_LSC)\r\nTSR | 1 |SM P U1 |W/m^2 |Top-of-atm. net Shortwave Radiation (+=dw)\r\nOLR | 1 |SM P U1 |W/m^2 |Outgoing Longwave Radiation (+=up)\r\nRADSWG | 1 |SM P L1 |W/m^2 |Net Shortwave Radiation at the Ground (+=dw)\r\nRADLWG | 1 |SM L1 |W/m^2 |Net Longwave Radiation at the Ground (+=up)\r\nHFLUX | 1 |SM L1 |W/m^2 |Sensible Heat Flux (+=up)\r\nEVAP | 1 |SM L1 |g/m^2/s |Surface Evaporation (g/m2/s)\r\nPRECON | 1 |SM P L1 |g/m^2/s |Convective Precipitation (g/m2/s)\r\nPRECLS | 1 |SM M1 |g/m^2/s |Large Scale Precipitation (g/m2/s)\r\nCLDFRC | 1 |SM P M1 |0-1 |Total Cloud Fraction (0-1)\r\nCLDPRS | 1 |SM PC167M1 |0-1 |Cloud Top Pressure (normalized)\r\nCLDMAS | 5 |SM P LL |kg/m^2/s |Cloud-base Mass Flux (kg/m^2/s)\r\nDRAG | 5 |SM P LL |kg/m^2/s |Surface Drag Coefficient (kg/m^2/s)\r\nWINDS | 1 |SM P L1 |m/s |Surface Wind Speed (m/s)\r\nTS | 1 |SM L1 |K |near Surface Air Temperature (K)\r\nQS | 1 |SM P L1 |g/kg |near Surface Specific Humidity (g/kg)\r\nENPREC | 1 |SM M1 |W/m^2 |Energy flux associated with precip. (snow, rain Temp)\r\nALBVISDF| 1 |SM P L1 |0-1 |Surface Albedo (Visible band) (0-1)\r\nDWNLWG | 1 |SM P L1 |W/m^2 |Downward Component of Longwave Flux at the Ground (+=dw)\r\nSWCLR | 5 |SM ML |K/s |Clear Sky Temp. Tendency due to Shortwave Radiation\r\nLWCLR | 5 |SM ML |K/s |Clear Sky Temp. Tendency due to Longwave Radiation\r\nTSRCLR | 1 |SM P U1 |W/m^2 |Clear Sky Top-of-atm. net Shortwave Radiation (+=dw)\r\nOLRCLR | 1 |SM P U1 |W/m^2 |Clear Sky Outgoing Longwave Radiation (+=up)\r\nSWGCLR | 1 |SM P L1 |W/m^2 |Clear Sky Net Shortwave Radiation at the Ground (+=dw)\r\nLWGCLR | 1 |SM L1 |W/m^2 |Clear Sky Net Longwave Radiation at the Ground (+=up)\r\nUFLUX | 1 |UM 184L1 |N/m^2 |Zonal Wind Surface Stress (N/m^2)\r\nVFLUX | 1 |VM 183L1 |N/m^2 |Meridional Wind Surface Stress (N/m^2)\r\nDTSIMPL | 1 |SM P L1 |K |Surf. Temp Change after 1 implicit time step\r\n\r\n</pre>\r\n</li>\r\n\r\n</ol>\r\n<p>\r\n\r\n\r\n\r\n\r\n\r\n\r\n</p><h2> B. Ocean </h2>\r\n\r\n<ol>\r\n <li> List of prognostic variables and tracers: \r\n\r\n <p> Velocities U and V, Temperature and Salinity. </p></li>\r\n\r\n <li> Main parametrisations. \r\n\r\n <p> Gent/McWiliams/Redi SGS Eddy Parameterization scheme and Nonlocal K-Profile Parameterization for Vertical Mixing of Large et al. [1994] describe in Large, W., J. McWilliams, and S. Doney, Oceanic vertical mixing: A review and a model with nonlocal boundary layer parameterization, Rev. Geophys., 32, 363-403, 1994. </p></li>\r\n\r\n<p> <br align=\"left\" />More on Ocean Parametrization packages in MITgcm: <a href=\"http://mitgcm.org/public/r2_manual/latest/online_documents/node239.html\"> \" here \"</a>\r\n\r\n</p><li> Model top \r\n <p>The upper surface of the ocean is a free surface which is driven by the divergence of volume flux (Boussinesq) in the interior. There are three treatments of the upper boundary available in MITgcm:\r\n</p><p> a. Rigid-lid approximation in which the upper surface is imagined to be an impermeable boundary\r\nwhich exerts a pressure on the fluid. \r\n</p><p> b. The linear free-surface which ignores some small terms in the depth integrated continuity equation,\r\nthat permits surface gravity waves to propagate with finite phase speed and introduces a Helmholtz term in the surface pressure equation when treated implicitly in time.\r\nThis is a very good approximation in deep water for whic.\r\n</p><p> c. The non-linear free-surface an un-approximated treatment of the upper surface </p></li>\r\n</ol>\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n<p> </p><h2> C. sea ice </h2>\r\n<p>The MITgcm sea ice model (MITgcm/sim) is based on a variant of the viscous-plastic (VP) dynamic-thermodynamic sea ice model [Zhang and Hibler, 1997] first introduced by Hibler [1980,1979]. In order to adapt this model to the requirements of coupled ice-ocean state estimation, many important aspects of the original code have been modified and improved: </p>\r\n<p>\r\n</p><pre> * the code has been rewritten for an Arakawa C-grid, both B- and C-grid variants are available; the C-grid code allows for no-slip and free-slip lateral boundary conditions;\r\n * two different solution methods for solving the nonlinear momentum equations have been adopted: LSOR [Zhang and Hibler, 1997], and EVP [Hunke and Dukowicz, 1997];\r\n * ice-ocean stress can be formulated as in Hibler and Bryan [1987] or as in Campin et al. [2008];\r\n * ice variables are advected by sophisticated, conservative advection schemes with flux limiting;\r\n * growth and melt parameterizations have been refined and extended in order to allow for more stable automatic differentiation of the code.\r\n</pre>\r\n\r\n<p>The sea ice model requires the following input fields: 10-m winds, 2-m air temperature and specific humidity, downward longwave and shortwave radiations, precipitation, evaporation, and river and glacier runoff. The sea ice model also requires surface temperature from the ocean model and the top level horizontal velocity. Output fields are surface wind stress, evaporation minus precipitation minus runoff, net surface heat flux, and net shortwave flux. The sea-ice model is global: in ice-free regions bulk formulae are used to estimate oceanic forcing from the atmospheric fields. </p>\r\n\r\n<p> <br align=\"left\" />More on Sea Ice packages in MITgcm: <a href=\"http://mitgcm.org/public/r2_manual/latest/online_documents/node251.html\"> \" here \"</a>\r\n \r\n\r\n\r\n\r\n </p><h2> D. Land / ice sheets </h2>\r\n\r\n<p> The land model is a simple two-layer model with prognostics temperature, liquid groundwater and snow height. There is no continental ice.\r\n</p><p> <br align=\"left\" />More on the land model package in MITgcm: <a href=\"http://mitgcm.org/public/r2_manual/latest/online_documents/node249.html\"> \" here \"</a>\r\n\r\n</p><h2> E. coupling details </h2>\r\n\r\n<p>1. frequency of coupling\r\n\r\n</p><p> Every ocean time step. \r\n\r\n</p><p>2. Are heat and water conserved by coupling scheme?\r\n\r\n</p><p>Yes.\r\n</p><p>3. list of variables passed between components:\r\n</p><p>\r\n<br align=\"left\" />More on the coupling package in MITgcm: <a href=\"http://mitgcm.org/public/r2_manual/latest/online_documents/node256.html\"> \" here \"</a>#\r\n\r\n </p><p>\r\n\r\n</p></div>", "modified": "2015-09-03", "ob_ref": 2870 }, { "id": 11319, "key": "moles2.provider", "value": "badc.nerc.ac.uk", "modified": "2022-06-16", "ob_ref": 37049 }, { "id": 11320, "key": "moles2.provider", "value": "badc.nerc.ac.uk", "modified": "2022-07-08", "ob_ref": 37313 }, { "id": 11321, "key": "moles2.provider", "value": "badc.nerc.ac.uk", "modified": "2022-07-08", "ob_ref": 13946 }, { "id": 11322, "key": "project.moles2_activity_subtype", "value": "dgActivityDataProject", "modified": "2026-01-29", "ob_ref": 20010 } ] }