Addressing the elephant in the room that wandered off to the village; Patterns of Asian elephant space-use and habitat preference

Bismay Tripathy –

Already an endangered species, the Asian Elephants (Elephas maximus) continue to be increasingly threatened by habitat degradation, poaching for ivory, and conflicts with people (Sukumar 2003; Menon et al., 2017). India harbours 60% of the current Asian elephant population, but only 23% of its elephant habitats reside within protected zones while the rest are perpetually disturbed by escalating anthropogenic pressures (such as expansion of human settlements and agriculture, livestock grazing and fuelwood gathering) and economic activities (mining, construction of road-railway networks etc.). Habitat degradation contributes to increasing elephant encounters with people and triggering human-elephant conflict (HEC). The conflict scenario in India escalates day by day gaining in severity and frequency.  In the four-year period between 2015 and 2018 alone, it had caused deaths of around 2,400 people and 490 elephants and annually, 0.5 million households suffered due to crop loss by elephant raiding from 2000 through 2010 (MOEF 2012; MoEF & CC, 2018). Elephants have the capacity to adapt to a mosaic of natural and modified habitats and their preference of habitat selection is often determined by the landscape composition as well as space and resource availability (such as vegetation and water). Thus, comprehension of elephants’ space-use with respect to their distribution is crucial for managing human-wildlife coexistence. We conducted our study on the space-use of elephants in the Keonjhar forest division in eastern India, where several hundreds of elephants have been killed as a result of electrocution, road-train mishaps, poaching and HEC.

Figure. 1: Pattern of estimated elephant occupancy, which was evaluated using the top model for occupancy probability.  Keonjhar forest division has seven forest ranges (Barbil, Bhuiyan-Juang Pihra (BJP), Champua, Ghatgaon, Keonjhar, Patna and Telkoi). Five elephant habitat cores (light blue color polygon) were identified and named as CFR, KFR, BFR, GFR and TFR

We used a popular species distribution technique called occupancy modeling, which analyzed the histories of elephant presence or absence on the survey sites (MacKenzie et al. 2017) to estimate the probability of elephant presence and underlying driving factors. For occupancy modeling, we used elephant GPS location at different sites along with anthropogenic and environmental variables, including climate variables such as precipitation data derived from monthly rain gauge data and mean annual temperature from MODIS-MOD11A1. Sentinel-2A satellite images were very helpful for extracting variables such as forests, cropland and settlements.

We observed elephant occupancy in 43% of the study region (about 2710 km2) (Figure 1) and occupancy was found to be higher in the regions with over 40% open forest cover (Figure 2B). It is easy to believe that a mega herbivore species like the elephants would prefer dense forests with minimum anthropogenic disturbances. However, we were surprised to find that elephants were actually drawn towards forests in human dominated landscapes with multiple land-use activities, over relatively intact forests (Sitompul et al., 2013; Huang et al. 2019). Scrubs and grasses, which are a primary forage of elephants, can grow easily in open forests as they receive better space and light conditions. Thus, open forests are the strongest variable influencing elephant occupancy, which specifically plays an important role in providing food and shelter for elephants as well as in their thermoregulation.

Figure. 2: Relationships between elephant detectability and the influential covariates

Furthermore, train-vehicle collisions have been one of the major causes of elephant mortality through the years (Jha et al., 2014; Dasgupta & Ghosh, 2015), so we evidenced a lower elephant occupancy in the regions with denser transportation networks (Figure. 2F). Even though crops are not natural forage for elephants, they preferred crops over grazing on natural forage, due to higher accessibility, palatability and nutrition (Sukumar, 1990; Campos-Arceiz et al., 2008). Thus, elephant detectability near croplands was relatively high.

When it comes to climate variables, we found a positive influence of precipitation on elephant detection, which was contrary to a study conducted in an extremely wet landscape of Southern India, that found how precipitation was the least influential covariate. However, we believe that favourable rainfall conditions improved water availability, while also increasing the productivity of deciduous forests with an abundance of palatable trees (Kumar et al., 2010; Jathanna et al., 2015), which attracted more elephants to these regions in the study area. Therefore, it is reasonable that variations in precipitation will be immediately reflected in the elephants’ space-use as rain-driven vegetation can prompt highly opportunistic elephant movement patterns.

Elephant sighting in an open forest
Elephant sighting in croplands

It is very challenging to demarcate exclusive regions for people and elephants within the varying landscapes of India where both human and elephant populations are high. However, owing to the presence of areas which are more frequently used by elephants such as the five habitat cores that we identified in our study (Figure 1), we can conclude that this region still has the potential to support a significant elephant population (Tripathy et al., 2021). Hence, for efficient landscape management and planning it is critical to understand the spatial factors that potentially influence the preference of space-use by elephants in this region which will in turn ensure peaceful coexistence between elephants and people while also facilitating elephant conservation strategies.

The Colour of Climate


Gristey, J.J., J.C. Chiu, R.J. Gurney, K.P. Shine, S. Havemann, J. Thelen, and P.G. Hill, 2019: Shortwave Spectral Radiative Signatures and Their Physical Controls. J. Climate, 32, 4805–4828,

Sunlight reaching the Earth is comprised of many different colours, or wavelengths. Some of these wavelengths cannot be detected by the human eye, such as the ultraviolet (UV) wavelengths which famously cause sunburn. Fortunately for us, the most intense sunlight is found at harmless visible wavelengths and reaches the surface with relative ease, allowing us to see during the daytime. Sometimes nature aligns to dramatically separate these wavelengths, producing beautiful optical phenomena such as rainbows. More often, however, the properties of the atmosphere and surface lead to intricate differences in the wavelengths of sunlight that get reflected back to space (Fig. 1).

Fig. 1. Schematic showing how the spectral structure of reflected sunlight at the top of the atmosphere can emerge via interactions with various atmospheric/surface properties*.

Satellites have observed specific wavelengths of reflected sunlight to infer the properties and evolution of our climate system for decades. Satellites have also independently measured the total amount of reflected sunlight across all wavelengths to track energy flows into and out of the Earth system. It has been less common to make spectrally resolved measurements at many contiguous wavelengths throughout the solar spectrum. In theory, these measurements would simultaneously provide the total energy flow – by integrating over the wavelengths – and the “spectral signature” associated with all atmospheric and surface properties that determined this energy flow. Our recent study puts this theory to the test.

Almost 100,000 spectra of reflected sunlight were computed at the top-of-atmosphere under a diverse variety of conditions. Applying a clustering technique to the computed spectra (which identifies “clusters” in a dataset with similar characteristics) revealed distinct spectral signatures. When we examined the atmospheric and surface properties that were used to compute the spectra belonging to each spectral signature, a remarkable separation of physical properties was found (Fig. 2).

Fig. 2. (top row) Three of the extracted “spectral signatures” of reflected sunlight. (bottom row) Their relationship to the underlying atmospheric/surface properties. Seven others are shown in the published article.

Surprisingly, the separation of physical properties by distinct spectral signatures, as shown in Fig. 2, was found to be robust up to the largest spatial scales tested of 240 km. This is similar to the footprint size of one of the only previous satellite instruments to measure contiguous spectrally resolved reflected sunlight, the SCIAMACHY**, providing an exciting opportunity to investigate spectral signature variability in real observations. We found that the frequency of spectral signatures in real SCIAMACHY observations followed the expected behaviour during the West African monsoon very closely (Fig. 3).

Fig. 3. (left) The annual cycle of precipitation [mm/day] associated with the West African monsoon, and (right) frequency of the three “spectral signatures” shown in Fig. 2 from real satellite observations during 2010 over West Africa.

Overall, the separation of physical properties by distinct spectral signatures demonstrates great promise for monitoring evolution of the Earth system directly from spectral reflected sunlight in the future.

Funding acknowledgement: This work was supported by the Natural Environment Research Council (NERC) SCience of the Environment: Natural and Anthropogenic pRocesses, Impacts and Opportunities (SCENARIO) Doctoral Training Partnership (DTP), Grant NE/L002566/ 1, and from the European Union 7th Framework Programme under Grant Agreement 603502 [EU project Dynamics–Aerosol–Chemistry–Cloud Interactions in West Africa (DACCIWA)]

*Note several key simplifications in Fig. 1 for the purposes of visual effect: atmospheric properties are separated, but often occur simultaneously and throughout the atmosphere; the depicted path of sunlight is one option, but sunlight emerging at the top of the atmosphere will come from many different paths; sunlight reflected by the surface will need to travel back through the same gases (and likely other properties) on its way back to the top of the atmosphere, which is not shown. The spectra in Fig. 1 are generated with SBDART using a set of arbitrary but realistic atmospheric and surface properties.

** SCIAMACHY = Scanning Imaging Absorption Spectrometer for Atmospheric Chartography.

Jake completed his PhD at Reading in 2018 and now works at the NOAA Earth System Research Laboratory (ESRL) in Boulder, Colorado.

Simulating measurements from the ISMAR radiometer using a new light scattering approximation


It is widely known that clouds pose a lot of difficulties for both weather and climate modelling, particularly when ice is present. The ice water content (IWC) of a cloud is defined as the mass of ice per unit volume of air. The integral of this quantity over a column is referred to as the ice water path (IWP) and is considered one of the essential climate variables by the World Meteorological Organisation. Currently there are large inconsistencies in the IWP retrieved from different satellites, and there is also a large spread in the amount produced by different climate models (Eliasson et al., 2011).
A major part of the problem is the lack of reliable global measurements of cloud ice. For this reason, the Ice Cloud Imager (ICI) will be launched in 2022. ICI will be the first instrument in space specifically designed to measure cloud ice, with channels ranging from 183 to 664 GHz. It is expected that the combination of frequencies available will allow for more accurate estimations of IWP and particle size. A radiometer called ISMAR has been developed by the UK Met Office and ESA as an airborne demonstrator for ICI, flying on the FAAM BAe-146 research aircraft shown in Fig. 1.

Figure 1: The Facility for Airborne Atmospheric Measurements (FAAM) aircraft which carries the ISMAR radiometer.

As radiation passes through cloud, it is scattered in all directions. Remote sensing instruments measure the scattered field in some way; either by detecting some of the scattered waves, or by detecting how much radiation has been removed from the incident field as a result of scattering. The retrieval of cloud ice properties therefore relies on accurate scattering models. A variety of numerical methods currently exist to simulate scattering by ice particles with complex geometries. In a very broad sense, these can be divided into 2 categories –
1: Methods that are accurate but computationally expensive
2: Methods that are computationally efficient but inaccurate

My PhD has involved developing a new approximation for aggregates which falls somewhere in between the two extremes. The method is called the Independent Monomer Approximation (IMA). So far, tests have shown that it performs well for small particle sizes, with particularly impressive results for aggregates of dendritic monomers.

Radiometers such as ICI and ISMAR convert measured radiation into brightness temperatures (Tb), i.e. the temperature of a theoretical blackbody that would emit an equivalent amount of radiation. Lower values of Tb correspond to more ice in the clouds, as a greater amount of radiation from the lower atmosphere is scattered on its way to the instrument’s detector (i.e. a brightness temperature “depression” is observed over thick ice cloud). Generally, the interpretation of measurements from remote-sensing instruments requires many assumptions to be made about the shapes and distributions of particles within the cloud. However, by comparing Tb at orthogonal horizontal (H) and vertical (V) polarisations, we can gain some information about the size, shape, and orientation of ice particles within the cloud. If large V-H polarimetric differences are measured, it is indicative of horizontally oriented particles, whereas random orientation produces less of a difference in signal. According to Gong and Wu (2017), neglecting the polarimetric signal could result in errors of up to 30% in IWP retrievals. Examples of Tb depressions and the corresponding V-H polarimetric differences can be seen in Fig. 2. In the work shown here, we explore this particular case further.

Figure 2: (a) ISMAR measured brightness temperatures, showing a depression (decrease in Tb) caused by thick cloud; (b) Polarimetric V-H brightness temperature difference, with significant values reaching almost 10 K.

Using the ISMAR instrument, we can test scattering models that could be used within retrieval algorithms for ICI. We want to find out whether the IMA method is capable of reproducing realistic brightness temperature depressions, and whether it captures the polarimetric signal. To do this, we look at a case study that was part of the NAWDEX (North Atlantic Waveguide and Downstream Impact Experiment) campaign of flying. The observations from the ISMAR radiometer were collected on 14 October 2016 off the North-West Coast of Scotland, over a frontal ice cloud. Three different aircraft took measurements from above the cloud during this case, which means that we have coincident data from ISMAR and two different radar frequencies of 35 GHz and 95 GHz. This particular case saw large V-H polarimetric differences reaching almost 10 K, as seen in Fig. 2(b). We will look at the applicability of the IMA method to simulating the polarisation signal measured from ISMAR, using the Atmospheric Radiative Transfer Simulator (ARTS).

For this study, we need to construct a model of the atmosphere to be used in the radiative transfer simulations. The nice thing about this case is that the FAAM aircraft also flew through the cloud, meaning we have measurements from both in-situ and remote-sensing instruments. Consequently, we can design our model cloud using realistic assumptions. We try to match the atmospheric state at the time of the in-situ observations by deriving mass-size relationships specific to this case, and generating particles to follow the derived relationship for each layer. The particles were generated using the aggregation model of Westbrook (2004).

Due to the depth of the cloud, it would not be possible to obtain an adequate representation of the atmospheric conditions using a single averaged layer. Hence, we modelled our atmosphere based on the aircraft profiles, using 7 different layers of ice with depths of approximately 1 km each. These layers are located between altitudes of 2 km and 9 km. Below 2 km, the Marshall-Palmer drop size distribution was used to represent rain, with an estimated rain rate of 1-2mm/hr taken from the Met Office radar. The general structure of our model atmosphere can be seen in Fig. 3, along with some of the particles used in each layer. Note that this is a crude representation and the figure shows only a few examples; in the simulations we use between 46 and 62 different aggregate realisations in each layer.

Figure 3: Examples of particles used in our model atmosphere. We represent the ice cloud using 3 layers of columnar aggregates and 4 layers of dendritic aggregates, and include a distribution of rain beneath the cloud.

To test our model atmosphere, we simulated the radar reflectivities at 35 GHz and 95 GHz using the particle models generated for this case. This allowed us to refine our model until sufficient accuracy was achieved. Then we used the IMA method to calculate the scattering quantities required by the ARTS radiative transfer model. These were implemented into ARTS in order to simulate the ISMAR polarisation observations.
Fig. 4 shows the simulated brightness temperatures using different layers of our modelled atmosphere, i.e. starting with the clear-sky case and gradually increasing the cloud amount. The simulations using the IMA scattering method in the ARTS model were compared to the measurements from ISMAR shown in Fig. 2. Looking at the solid lines in Fig. 4, it can be seen that the aggregates of columns and dendrites simulate the brightness temperature depression well, but do not reproduce the V-H polarization signal. Thus we decided to include some horizontally aligned single dendrites which were not included in our original atmospheric model. The reason we chose these particles is that they tend to have a greater polarization signal compared to aggregates, and there was evidence in the cloud particle imagery that they were present in the cloud during the time of interest. We placed these particles at the cloud base, without changing the ice water content of the model. The results from that experiment are shown by the diagonal crosses in Fig. 4. It is clear that adding single dendrites allow us to simulate a considerably larger polarimetric signal, closely matching the ISMAR measurements. Using only aggregates of columns and dendrites gives a V-H polarimetric difference of 1.8K, whereas the inclusion of dendritic particles increases this value to 8.4K.

Figure 4: Simulated brightness temperatures using different layers of our model atmosphere. Along the x-axis we start with the clear-sky case, followed by the addition of rain. Then we add one layer of cloud at a time, starting from the top layer of columnar aggregates.

To conclude, we have used our new light scattering approximation (IMA) along with the ARTS radiative transfer model to simulate brightness temperature measurements from the ISMAR radiometer. Although the measured brightness temperature depressions can generally be reproduced using the IMA scattering method, the polarisation difference is very sensitive to the assumed particle shape for a given ice water path. Therefore, to obtain good retrievals from ICI, it is important to represent the cloud as accurately as possible. Utilising the polarisation information available from the instrument could provide a way to infer realistic particle shapes, thereby reducing the need to make unrealistic assumptions.


Eliasson, S., S. A. Buehler, M. Milz, P. Eriksson, and V. O. John, 2011: Assessing observed and modelled spatial distributions of ice water path using satellite data. Atmos. Chem. Phys., 11, 375-391.

Gong, J., and D. L. Wu, 2017: Microphysical properties of frozen particles inferred from Global Precipitation Measurement (GPM) Microwave Imager (GMI) polarimetric measurements. Atmos. Chem. Phys., 17, 2741-2757.

Westbrook, C. D., R. C. Ball, P. R. Field, and A. J. Heymsfield, 2004: A theory of growth by differential sedimentation with application to snowflake formation. Phys. Rev. E, 70, 021403.

Investigating the use of early satellite data to test historical reconstructions of sea surface temperature


Observations of sea surface temperature (SST) form one of the key components of the climate record. There are a number of different in-situ based reconstructions of SST extending back over 150 years, but they are not truly independent of each other because the observations they are based on are largely the same (Berry et al., 2018). Datasets of SST retrieved from satellite radiometers exist for the 1980s onwards, providing an independent record of SST. Before this, SST reconstructions are based on sparse, ship-based measurements.

There were meteorological measurements being made from satellites in the 1960s and 70s, however, some of which can potentially be used to retrieve SST. My PhD focuses on investigating if we can retrieve SST from one of these early satellite instruments, to test the reliability of the SST reconstructions before the 1980s. This instrument is the Infrared Interferometer Spectrometer (IRIS), which made measurements of atmospheric emission spectra on-board the Nimbus 4 satellite from April 1970 to January 1971. IRIS had over 800 thermal infrared (IR) channels, covering the 400-1600 cm-1 spectral region. Figure 1 shows an example of two typical IRIS radiance spectra, with the main spectral absorption features labelled as well as the atmospheric window regions, which are the main spectral regions used for SST retrieval.

Figure 1: Example of two typical IRIS radiance spectra; the main spectral absorption features are labelled as well as the atmospheric window regions.

Before using the IRIS data to retrieve SST, it was necessary to apply a series of quality assurance tests to filter out bad data. A few months into my PhD, work by Bantges et al. (2016) revealed evidence for a wavelength dependent cold bias of up to 2K in the data. A large part of my PhD was spent trying to quantify this bias. This was done by comparing clear-sky IRIS spectra with spectra simulated with a radiative transfer model. Unfortunately, this meant that the SSTs eventually retrieved from IRIS are not totally independent of the SST reconstructions as the simulations are based on reanalysis data forced by the HadISST2 reconstruction. Figure 2 compares our estimate of the IRIS spectral bias with globally averaged spectral differences between IRIS, the Interferometric Monitor for Greenhouse Gases (IMG) and the Infrared Atmospheric Sounding Instrument (IASI) from Bantges et al. (2016). This shows close agreement between our bias estimate and the IRIS-IMG and IRIS-IASI differences outside of the ozone spectral region, which is not relevant for SST retrieval.

It cannot just be assumed that the bias is the same for each IRIS measurement. Comparison of IRIS (bias-corrected using our initial bias estimate) with window channel data from the Temperature-Humidity Infrared Radiometer (THIR), also on-board Nimbus 4, reveals that the relative IRIS-THIR bias varies with window brightness temperature and orbit angle. The THIR, however, may have biases of its own, so these biases cannot be attributed to IRIS.

Figure 2: Area-weighted global mean brightness temperature difference averaged over AMJ between IRIS (1970), IMG (1997) and IASI (2012) (black and blue lines) from Bantges et al. (2016), compared with our IRIS bias estimate, also area-weighted and averaged over AMJ (red line). The ozone absorption band is not used for SST retrieval, so is shaded grey.

The technique of optimal estimation was applied to retrieve SST from IRIS. This uses the observation-simulation differences together with information about the sensitivity of the simulations to the state of the atmosphere and ocean to estimate the SST. IR satellite retrievals of SST are usually performed in clear-sky conditions only. However, the low spatial resolution of IRIS means that very few cases are fully clear-sky. For this reason, we had to adapt the retrieval method to be tolerant of some cloud. This involves retrieving SST simultaneously with cloud fraction (CF). The retrieval method was then tested on partly cloudy (≤0.2 CF) IASI spectra made ‘IRIS-like’ by spatial averaging, spectral smoothing and simulating IRIS-like errors. The retrieved IRIS-like SSTs were validated against quality-controlled drifting buoy SSTs. This revealed latitudinal biases in the retrieved SSTs for the partly cloudy cases, not present in the SSTs for clear-sky cases.

SSTs were then retrieved for all IRIS cases with an expected CF ≤ 0.2. Figure 3 shows the difference between the gridded, monthly average IRIS SSTs and two of the SST reconstructions (HadSST3 and HadISST2) for July 1970. There are large, spatially correlated differences between the IRIS SSTs and reconstructions. We expect a latitudinal bias in the IRIS SSTs and some level of remaining bias in the IRIS spectra is likely, contributing to further SST bias. It is therefore likely that the differences in Figure 3 are mainly due to bias in the IRIS SSTs rather than the reconstructions.

Figure 3: Gridded IRIS-HadSST3 (left) and IRIS-HadISST2 (right) SST for July 1970. HadISST2 is a globally complete, interpolated dataset whereas HadSST3 is not globally complete.

Despite being unable to retrieve bias-free SST estimates from IRIS, my work has contributed towards better understanding the characteristics of IRIS. This ties in with a current project aiming to recover and assess the quality of data from a number of different historic satellite sensors, including IRIS, for assimilation in the next generation of climate reanalyses.


Bantges, R., H. Brindley, X. H. Chen, X. L. Huang, J. Harries, J. Murray (2016), On the detection of robust multi-decadal changes in the Earth’s Outgoing Longwave Radiation spectrum. J. Climate, 29, 4939-4947.

Berry, D. I., G. K. Corlett, O. Embury, C. J. Merchant (2018), Stability assessment of the (A)ATSR Sea Surface Temperature climate dataset from the European Space Agency Climate Change Initiative. Remote Sens., 10, 126.

VMSG and COMET 2018 (or a Tale of Two Conferences)

The Volcanic and Magmatic Studies Group (VMSG) held a conference from the 3-6th of January in Leeds. The Centre for Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET) held a student conference from 8-9th January in Cambridge. It was a conference double-whammy about all things volcanic – heaven!

VMSG is a joint special interest group of the Mineralogical Society of Great Britain and Ireland and the Geological Society of London. The VMSG conference is a fairly small affair, with about 200 in attendance, and it brings together research in geochemistry, seismology, volcanology and related fields. Because of its size, it’s a nice informal space where there is a focus on students presenting their work to the VMSG community, but anyone is free to present their research.

Talks ranged from how tiny fossils, called diatoms, became trapped in a pyroclastic density current, to modelling of lava domes, to how local people interact with the volcano they live on at Masaya, to every aspect of volcanology you can think of. The final talk was definitely a highlight – with everyone in 3D glasses to look at volcanic plumes across Russia, it really brought the satellite images to life (and we got to keep the glasses).

90 posters on a variety of topics were presented, the majority of which were by students (I was one of them). There was of course an obligatory dinner and disco to round off the second day of talks, and a great chance to network with other people from VMSG.

For the best poster title of the conference, you need look no further than this gem.

The conference also provides workshops on different aspects of research, with sessions on writing papers, diffusion modelling and InSAR to name a few. These were hosted on the 6th at the University of Leeds Environment and Earth Sciences Department, and comprised a full day of talks and labs so you could get to grips with the techniques you were being shown. I attended the InSAR workshop, which gave a good introduction to the topic of comparing two satellite images and seeing where the ground had moved. There was also a session on deformation modelling in the afternoon and playing with bits of code.

An afternoon of modelling InSAR deformations and code – hill-arity ensued.

Then it was onto the second leg of the conferences, which took the action to Cambridge, where students that are part of COMET met up to discuss work and attend talks from 8-9th January.

Gneiss weather in Cambridge!

COMET is a National Environment Research Council Centre of Excellence, it comprises a group of researchers that uses remote and ground sensed data and models to study earthquakes and volcanoes. They also work with the British Geological Survey and the European Space Agency, and fund PhD projects in related fields.

The meet-up of students comprised two days of talks from students, with some keynote speakers who had been past members of COMET that had gone on to careers outside of academia. The talks from second and third years included: remote sensing and InSAR being used to examine tectonic strain in the East African Rift Valley and slip (movement) rates along faults in Tibet, modelling how gas bubbles in magma change the more crystals you add to the magma, and using cosmogenic isotopes to work out slip rates on a fault in Italy.

The Department had cabinets and cabinets of samples that rocked.

First years are also given the chance to give a talk lasting 5 minutes, so I filled people in on what I’d been up to in the past four months – lots of data collection! My project will be using satellite data to look at the varied eruption behaviour of Bagana volcano in Papua New Guinea, with a view to modelling this behaviour to better understand what causes it. Bagana has a tendency to send out thick lava flows in long pulses and let out lots of gas, and occasionally then violently erupt and let out lots of ash and hot pyroclastic density currents. But it is very understudied, as it is so remote – so there’s lots still to be learnt about it!

Me with my poster (I’ve run out of geology puns).

The meet-up also included a fancy meal in Pembroke College’s Old Library, with candles and it felt a bit like being at Hogwarts! Then it was back to Reading, thoroughly worn out, but with lots of ideas and many useful contacts – VMSG2019 is in St. Andrews and I can’t wait.

Understanding our climate with tiny satellites

Gristey, J. J., J. C. Chiu, R. J. Gurney, S.-C. Han, and C. J. Morcrette (2017), Determination of global Earth outgoing radiation at high temporal resolution using a theoretical constellation of satellites, J. Geophys. Res. Atmos., 122, doi:10.1002/2016JD025514.

Email:          Web:

The surface of our planet has warmed at an unprecedented rate since the mid-19th century and there is no sign that the rate of warming is slowing down. The last three decades have all been successively warmer than any preceding decade since 1850, and 16 of the 17 warmest years on record have all occurred since 2001. The latest science now tells us that it is extremely likely that human influence has been the dominant cause of the observed warming1, mainly due to the release of carbon dioxide and other greenhouse gases into our atmosphere. These greenhouse gases trap heat energy that would otherwise escape to space, which disrupts the balance of energy flows at the top of the atmosphere (Fig. 1). The current value of the resulting energy imbalance is approximately 0.6 W m–2, which is more than 17 times larger than all of the energy consumed by humans2! In fact, observing the changes in these energy flows at the top of the atmosphere can help us to gauge how much the Earth is likely to warm in the future and, perhaps more importantly, observations with sufficient spatial coverage, frequency and accuracy can help us to understand the processes that are causing this warming.

Figure 1. The Earth’s top-of-atmosphere energy budget. In equilibrium, the incoming sunlight is balanced by the reflected sunlight and emitted heat energy. Greenhouse gases can reduce the emitted heat energy by trapping heat in the Earth system leading to an energy imbalance at the top of the atmosphere.

Observations of energy flows at the top of the atmosphere have traditionally been made by large and expensive satellites that may be similar in size to a large car3, making it impractical to launch multiple satellites at once. Although such observations have led to many advancements in climate science, the fundamental sampling restrictions from a limited number of satellites makes it impossible to fully resolve the variability in the energy flows at the top of atmosphere. Only recently, due to advancements in small satellite technology and sensor miniaturisation, has a novel, viable and sustainable sampling strategy from a constellation of satellites become possible. Importantly, a constellation of small satellites (Fig. 2a), each the size of a shoe-box (Fig. 2b), could provide both the spatial coverage and frequency of sampling to properly resolve the top of atmosphere energy flows for the first time. Despite the promise of the constellation approach, its scientific potential for measuring energy flows at the top of the atmosphere has not been fully explored.

Figure 2. (a) A constellation of 36 small satellites orbiting the Earth. (b) One of the small “CubeSat” satellites hosting a miniaturised radiation sensor that could be used [edited from earthzine article].
To explore this potential, several experiments have been performed that simulate measurements from the theoretical constellation of satellites shown in Fig 2a. The results show that just 1 hour of measurements can be used to reconstruct accurate global maps of reflected sunlight and emitted heat energy (Fig. 3). These maps are reconstructed using a series of mathematical functions known as “spherical harmonics”, which extract the information from overlapping samples to enhance the spatial resolution by around a factor of 6 when compared with individual measurement footprints. After producing these maps every hour during one day, the uncertainty in the global-average hourly energy flows is 0.16 ± 0.45 W m–2 for reflected sunlight and 0.13 ± 0.15 W m–2 for emitted heat energy. Observations with these uncertainties would be capable of determining the sign of the 0.6 W m–2 energy imbalance directly from space4, even at very short timescales.

Figure 3. (top) “Truth” and (bottom) recovered enhanced-resolution maps of top of atmosphere energy flows for (left) reflected sunlight and (right) emitted heat energy, valid for 00-01 UTC on 29th August 2010.

Also investigated are potential issues that could restrict similar uncertainties being achieved in reality such as instrument calibration and a reduced number of satellites due to limited resources. Not surprisingly, the success of the approach will rely on calibration that ensures low systematic instrument biases, and on a sufficient number of satellites that ensures dense hourly sampling of the globe. Development and demonstration of miniaturised satellites and sensors is currently underway to ensure these criteria are met. Provided good calibration and sufficient satellites, this study demonstrates that the constellation concept would enable an unprecedented sampling capability and has a clear potential for improving observations of Earth’s energy flows.

This work was supported by the NERC SCENARIO DTP grant NE/L002566/1 and co-sponsored by the Met Office.


1 This statement is quoted from the latest Intergovernmental Panel on Climate Change assessment report. Note that these reports are produced approximately every 5 years and the statements concerning human influence on the climate have increased in confidence in every report.

2 Total energy consumed by humans in 2014 was 13805 Mtoe = 160552.15 TWh. This is an average power consumption of 160552.15 TWh  / 8760 hours in a year = 18.33 TW

Rate of energy imbalance per square metre at top of atmosphere is = 0.6 W m–2. Surface area of “top of atmosphere” at 80 km is 4 * pi * ((6371+80)*103 m)2 = 5.23*1014 m2. Rate of energy imbalance for entire Earth = 0.6 W m–2 * 5.23*1014 m2 = 3.14*1014 W = 314 TW

Multiples of energy consumed by humans = 314 TW / 18.33 TW = 17

3 The satellites currently carrying instruments that observe the top of atmosphere energy flows (eg. MeteoSat 8, Aqua) will typically also be hosting a suite of other instruments, which adds to the size of the satellite. However, even the individual instruments are still much larger that the satellite shown in Fig. 2b.

4 Currently, the single most accurate way to determine the top-of-atmosphere energy imbalance is to infer it from changes in ocean heat uptake. The reasoning is that the oceans contain over 90% of the heat capacity of the climate system, so it is assumed on multi-year time scales that excess energy accumulating at the top of the atmosphere goes into heating the oceans. The stated value of 0.6 W m–2 is calculated from a combination of ocean heat uptake and satellite observations.


Allan et al. (2014), Changes in global net radiative imbalance 1985–2012, Geophys. Res. Lett., 41, 5588–5597, doi:10.1002/2014GL060962.

Barnhart et al. (2009), Satellite miniaturization techniques for space sensor networks, Journal of Spacecraft and Rockets46(2), 469–472, doi:10.2514/1.41639.

IPCC (2013), Climate Change 2013: The Physical Science Basis, available online at

NASA (2016), NASA, NOAA Data Show 2016 Warmest Year on Record Globally, available online at

Sandau et al. (2010), Small satellites for global coverage: Potential and limits, ISPRS J. Photogramm., 65, 492–504, doi:10.1016/j.isprsjprs.2010.09.003.

Swartz et al. (2013), Measuring Earth’s Radiation Imbalance with RAVAN: A CubeSat Mission to Measure the Driver of Global Climate Change, available online at

Swartz et al. (2016), The Radiometer Assessment using Vertically Aligned Nanotubes (RAVAN) CubeSat Mission: A Pathfinder for a New Measurement of Earth’s Radiation Budget. Proceedings of the AIAA/USU Conference on Small Satellites, SSC16-XII-03