1 km resolution composite data from the Met Office's UK rainfall radars via the Met Office NIMROD system. The NIMROD system is a very short range forecasting system used by the Met Office. Data are available from 2004 until present at UK stations and detail rain-rate observations taken every 5 minutes. Each file has been compressed and then stored within daily tar archive files.

The precipitation rate analysis uses processed radar and satellite data, together with surface reports and Numerical Weather Prediction (NWP) fields. The UK has a network of 15 C-band rainfall radars and data form these are processed by the Met Office NIMROD system.

Please note CEDA are not able to fulfil requests for missing data from this archive. The data may be available at a cost by contacting the Met Office directly with required dates. It is worth contacting the CEDA first to check if the reason for the gap is already identified as being due to the data not existing at all.

CEDA does not support reading software but programs written by the community to do this task in IDL, Matlab, FORTRAN and Python are available in the dataset software directory.

The data files contain integer precipitation rates in unit of (mm/hr)*32. Each value is between 0 and 32767. In practice it is rare to see a value in excess of 4096 i.e. 128 mm/hr.

At 10:00 on 14 June 2005, the 1 km composite data files became larger with 2175 rows by 1725 columns compared to the previous 775 rows by 640 columns. From 14:55 on 30 August 2006, the 1 km composite data files are gzipped files. From 13 Nov 2007, the 1 km composite is derived directly from processed polar (600m x 1 degree) rain rate estimates and there is more detail in the rain structure.

Data from the NIMROD system data describe rain-rate observations in Northern Europe taken by NIMROD, which is a very short range forecasting system used by the Met Office. Composite European data are available from April 2002 until present, collected by a network of rain radars at northern European stations. Radar images from the 15 C-band (5.3 cm wavelength) radars around Europe at 5 km resolution, are received by the Nimrod system at 15 minute intervals. Data products are available since April 2002, whilst image products are available from February 2003. Each file has been compressed and then stored within daily tar archive files.

The precipitation rate analysis uses processed radar and satellite data, together with surface reports and Numerical Weather Prediction (NWP) fields. Europe has a network of 15 C-band rainfall radars and data form these are processed by the Met Office NIMROD system. The data files contain integer precipitation rates in unit of (mm/hr)*32. Each value is between 0 and 32767. In practice it is rare to see a value in excess of 4096 i.e. 128 mm/hr

CEDA are not able to fulfil requests for data that are missing from this archive. The data may be available at a cost by contacting the Met Office directly with required dates. It is worth contacting CEDA first to check if the reason for the gap is already identified as being due to the data not existing at all.

A bathymetric survey of Nimrod Lake, Arkansas, was conducted in late April to mid-May, 2016, by the Lower Mississippi-Gulf Water Science Center (LMG WSC) of the U.S. Geological Survey (USGS) using methodologies for sonar surveys similar to those described by Wilson and Richards (2006) and Richards and Huizinga (2018). Point data from the bathymetric survey were merged with point data from an aerial LiDAR survey conducted in December, 2010, that were provided by the U.S. Army Corps of Engineers (USACE), Little Rock District. From the combined point dataset, a terrain dataset (a type of triangulated irregular network, or TIN, model) was created in Esri ArcGIS, version 10.5, for the area within the approximate extent of the flood pool of the lake as defined by the 374-ft contour of the LiDAR data. Products in this data release are stored within an Esri file geodatabase and include the following: Feature classes of point data from the bathymetric and LiDAR surveys; the terrain dataset; a feature class of bathymetric contours at 4-ft intervals; and a table of storage capacity (volume) of the lake at 1-ft increments in water surface elevation from 301-373 ft above the North American Vertical Datum of 1988 (NAVD88), seasonal conservation pool elevations 342.13, 344.63, and 345.13 NAVD88, and flood pool elevation 373.13 ft NAVD88. References: Richards, J.M. and Huizinga, R.J., 2018, Bathymetric contour map, surface area and capacity table, and bathymetric difference map for Clearwater Lake near Piedmont, Missouri, 2017: U.S. Geological Survey Scientific Investigations Map 3409: 1 sheet, https://doi.org/10.3133/sim3409; Wilson, G.L., and Richards, J.M., 2006, Procedural Documentation and Accuracy Assessment of Bathymetric Maps and Area/Capacity Tables for Small Reservoirs: U.S. Geological Survey Scientific Investigations Report 2006-5208, https://pubs.usgs.gov/sir/2006/5208/.

The U.S. Geological Survey collected topographic data in cooperation with the U.S. Army Corps of Engineers to assist in the management of two recreation areas at Blue Mountain Lake and near Nimrod Lake in Arkansas. Data were collected March 3-4, 2020 using terrestrial light detection and ranging (T-lidar) surveying equipment, Global navigation Satellite System (GNSS) surveying equipment, and conventional surveying techniques. The surveyed locations had a parking area, boat ramp, and some small recreational structures. Each site was surveyed using a FARO 3D tripod-mounted T-lidar unit. Additionally, a topographic survey was conducted in order to georeference the lidar survey. These topographic data were collected by conventional surveying using a total station and real-time kinematic (RTK) surveying using Trimble R8 and Trimble R10 GNSS surveying equipment and standard methods (Rydlund and Densmore, 2012). Differential corrections for the RTK surveys were obtained from the Arkansas Department of Transportation real-time network, which allowed real-time survey grade horizontal and vertical location information to be determined for one control point in each survey area. The T-lidar survey data are available in LAS format.

It has been proposed that neural noise in the cortex arises from chaotic dynamics in the balanced state: in this model of cortical dynamics, the excitatory and inhibitory inputs to each neuron approximately cancel, and activity is driven by fluctuations of the synaptic inputs around their mean. It remains unclear whether neural networks in the balanced state can perform tasks that are highly sensitive to noise, such as storage of continuous parameters in working memory, while also accounting for the irregular behavior of single neurons. Here we show that continuous parameter working memory can be maintained in the balanced state, in a neural circuit with a simple network architecture. We show analytically that in the limit of an infinite network, the dynamics generated by this architecture are characterized by a continuous set of steady balanced states, allowing for the indefinite storage of a continuous parameter. In finite networks, we show that the chaotic noise drives diffusive motion along the approximate attractor, which gradually degrades the stored memory. We analyze the dynamics and show that the slow diffusive motion induces slowly decaying temporal cross correlations in the activity, which differ substantially from those previously described in the balanced state. We calculate the diffusivity, and show that it is inversely proportional to the system size. For large enough (but realistic) neural population sizes, and with suitable tuning of the network connections, the proposed balanced network can sustain continuous parameter values in memory over time scales larger by several orders of magnitude than the single neuron time scale.

We propose to study the influence of deposition rates and annealing effects on microporous amorphous solid water samples (ASW) using NIMROD. The data will allow us to monitor the kinetics of the collapse of the micropore network with time and temperature and understand at the same time the mesoscale structure quantitatively in terms of pore size distribution, total pore surface area, etc. The unique advantage of NIMROD allowing for the first time such an analysis is the rapid collection time, which prevents contamination of the bulk samples with background gas, paired with an accessible Q-range covering both micro- and mesostructure aspects. The results will be of importance in understanding the properties of ASW, the most commonly encountered form of water in the universe, and the pore collapse impacts on the chemistry and physics that can be occurring during planet and star formation.

We performed a field survey to characterize diatom communities living in the benthic microbial mats of ponds across the McMurdo Sound region of Antarctica. Samples were collected during the Austral summers of 2012-13, 2013-14, and 2015-16 from the McMurdo Dry Valleys, Cape Royds, and from Hut Ridge near McMurdo Station. We also characterized pond diatom communities in samples collected on various dates from 1908-1909 during Ernest Shackleton’s Nimrod Expedition to compare against modern samples. Historical samples were taken from Cape Royds and the Stranded Moraines, and were analyzed from slides stored at the Natural History Museum, London. This data package includes relative abundance data for diatom species found in these modern and historical samples.

This dataset consists of 3D spatial grids of weather radar reflectivity, which have 5-minute temporal, 1km horizontal, and 500m vertical resolution. They are constructed from UK weather radar network scans, provided by 16 radars in England, Scotland, Wales, Northern Ireland, and the Channel Islands.

In addition to the 3D, there are some 2D grids of fields derived from the vertical grid columns, including maximum column dBZ and vertically integrated liquid water. Please see descriptions below.

Note – this dataset contains non-operational data products, with this time-limited dataset provided primarily to aid use within the ParaChute research programme.

The interpolation method used to arrive at the multi-radar gridded values is similar to that described in Zhang (2005). The reasons for choosing this method over another more recent one (Scovell and al-Sakka, 2016) can be found in Stein et al. (2020).

The horizontal domain spans X=[-405000, 1320000], Y=[-625000, 1550000] metres on the UK National Grid (EPSG:27700) projection. This is regularly spaced, with 2175 rows x 1725 columns, and is the same as the “Nimrod” grid used by RadarNet (Harrison et al., 2009). Grid points are located at the centres of each grid box (at X/Y coordinates ending in 500). The vertical is comprised of 24 evenly spaced 500m height levels in the range h=[250,11750] metres AMSL, with the first at 250m AMSL.

The data are temporally continuous, at 5-minute resolution, from 2023-06-01 00:00 UTC to 2023-08-31 23:55 UTC. An exception being for two periods of network outage, which are 2023-06-12 17:00-19:00 UTC, and 2023-08-14 08:00-09:00 UTC.

The 3D radar grids are formed using scan data following the operational scanning strategy of the UK. This favours low elevation angles, to aid with surface quantitative precipitation estimation. Thus, at higher altitudes, coverage can be sparse (Scovell and al-Sakka, 2016) and the observations are of relatively poor quality, being at long range. No 3D grid point has a data value that has been extrapolated beyond 2.5km range horizontally. Thus, there are large data voids ~10km, at the highest altitude levels. Smaller gaps can appear at lower altitudes. At the lowest levels, and at long range from a radar site, there may sometimes be no coverage. This is unavoidable, due to the curvature of the Earth.

DATASETS The data are stored in an HDF5 file format, with the standard HDF5-native gzip compression. The stored attributes and datasets are based on, but do not strictly adhere to, the ODIM data model specification (Michelson et al., 2008). The following ODIM quantities encoded: • DBZH: 3D reflectivity composite • ZDR: 3D ZDR composite • RHOHV: 3D Fisher-Z (arctanh) -transformed RHOHV composite • MAXDBZ: 2D “column maximum” , derived from DBZH. In numpy these are computed with np.max ( reflectivity, axis = 0) • VIL: 2D Vertically Integrated Liquid water, as in Green and Clarke (1972) • TOP45, TOP18: echo top heights (highest height level) for DBZH > 45/18 • POH: Probability of Hail; equal to f * ( TOP45 – height of T=0C isotherm ), as in DeLobbe and Holleman (2003). • VII, CRIT_IND: Vertically Integrated Ice and (lightning) Criterion Index, as defined in Mosier et al. (2011), and Haklander (2014). • SHI, POSH, MEHS: these are hail and lightning indices derived from formulae in Witt et al. (c. 1998) The following caveats apply to the ODIM formatting: • Unofficial non-compliant ODIM attributes have been added to allow storage of 3D information in the ODIM HDF5 format. • The metadata describing the 3D grids are not complete.

See the online resources section for full citations used on this record.