Evaluating the value of a network of cosmic-ray probes for improving land surface modelling

Land surface models can model matter and energy fluxes between the land surface and atmosphere, and provide a lower boundary condition to atmospheric circulation models. For these applications, accurate soil moisture quantification is highly desirable but not always possible given limited observations and limited subsurface data accuracy. Cosmic-ray probes (CRPs) offer an interesting alternative to indirectly measure soil moisture and provide an observation that can be assimilated into land surface models for improved soil moisture prediction. Synthetic studies have shown the potential to estimate subsurface parameters of land surface models with the assimilation of CRP observations. In this study, the potential of a network of CRPs for estimating subsurface parameters and improved soil moisture states is tested in a real-world case scenario using the local ensemble transform Kalman filter with the Community Land Model. The potential of the CRP network was tested by assimilating CRP-data for the years 2011 and 2012 (with or without soil hydraulic parameter estimation), followed by the verification year 2013. This was done using (i) the regional soil map as input information for the simulations, and (ii) an erroneous, biased soil map. For the regional soil map, soil moisture characterization was only improved in the assimilation period but not in the verification period. For the biased soil map, soil moisture characterization improved in both periods strongly from a ERMS of 0.11 cm3/cm3 to 0.03 cm3/cm3 (assimilation period) and from 0.12 cm3/cm3 to 0.05 cm3/cm3 (verification period) and the estimated soil hydraulic parameters were after assimilation closer to the ones of the regional soil map. Finally, the value of the CRP network was also evaluated with jackknifing data assimilation experiments. It was found that the CRP network is able to improve soil moisture estimates at locations between the assimilation sites from a ERMS of 0.12 cm3/cm3 to 0.06 cm3/cm3 (verification period), but again only if the initial soil map was biased

The NeST (Neoadjuvant systemic therapy in breast cancer) study: National Practice Questionnaire of United Kingdom multi-disciplinary decision making

Abstract: Background: Neoadjuvant systemic therapy (NST) is increasingly used in the treatment of breast cancer, yet it is clear that there is significant geographical variation in its use in the UK. This study aimed to examine stated practice across UK breast units, in terms of indications for use, radiological monitoring, pathological reporting of treatment response, and post-treatment surgical management. Methods: Multidisciplinary teams (MDTs) from all UK breast units were invited to participate in the NeST study. A detailed questionnaire assessing current stated practice was distributed to all participating units in December 2017 and data collated securely usingREDCap. Descriptive statistics were calculated for each questionnaire item. Results: Thirty-nine MDTs from a diverse range of hospitals responded. All MDTs routinely offered neoadjuvant chemotherapy (NACT) to a median of 10% (range 5–60%) of patients. Neoadjuvant endocrine therapy (NET) was offered to a median of 4% (range 0–25%) of patients by 66% of MDTs. The principal indication given for use of neoadjuvant therapy was for surgical downstaging. There was no consensus on methods of radiological monitoring of response, and a wide variety of pathological reporting systems were used to assess tumour response. Twenty-five percent of centres reported resecting the original tumour footprint, irrespective of clinical/radiological response. Radiologically negative axillae at diagnosis routinely had post-NACT or post-NET sentinel lymph node biopsy (SLNB) in 73.0 and 84% of centres respectively, whereas 16% performed SLNB pre-NACT. Positive axillae at diagnosis would receive axillary node clearance at 60% of centres, regardless of response to NACT. Discussion: There is wide variation in the stated use of neoadjuvant systemic therapy across the UK, with general low usage of NET. Surgical downstaging remains the most common indication of the use of NAC, although not all centres leverage the benefits of NAC for de-escalating surgery to the breast and/or axilla. There is a need for agreed multidisciplinary guidance for optimising selection and management of patients for NST. These findings will be corroborated in phase II of the NeST study which is a national collaborative prospective audit of NST utilisation and clinical outcomes

Antarctica: Phantom of the Past or Canary in the Cage?

"A bridge to our future and a window on our past." This was a phrase used by President Clinton to describe Antarctica when he spoke at the International Antarctic Centre recently. It sums up the view that, in essence, Antarctica holds a key to our greater understanding of the Earth's dynamics, and that the past and the future of the continent, and the earth, are intrinsically linked. The "Phantom of the past" and the 'Canary in the cage" are thus both useful metaphors for describing the significance of Antarctica in the global context. Phantom Of the past: The "Phantom of the Past" metaphor refers to Antarctica as a library of information about the evolution Of our planet. The 'books' are covered in dust and we have only read a few pages of the numerous volumes, but they contain a wealth of information, most of which we have yet to fully comprehend. For example, the phantom presents us with information gathered from such research as the Cape Roberts project and deep ice core drilling, which reveal past climatic events from which to gauge current and possible future trends. According to Tim Naish, of the Imstitute of Geological and Nuclear Sciences: "We have moved from a phase of scientific exploration to one of realisation that much of what we are learning about Antarctica and the Southern Ocean has major implications for understanding the past and future of our planet" (Naish, 1999). "A bridge to our future and a window on our past." This was a phrase used by President Clinton to describe Antarctica when he spoke at the International Antarctic Centre recently. It sums up the view that, in essence, Antarctica holds a key to our greater understanding of the Earth's dynamics, and that the past and the future of the continent, and the earth, are intrinsically linked. The "Phantom of the past" and the 'Canary in the cage" are thus both useful metaphors for describing the significance of Antarctica in the global context. Phantom Of the past: The "Phantom of the Past" metaphor refers to Antarctica as a library of information about the evolution Of our planet. The 'books' are covered in dust and we have only read a few pages of the numerous volumes, but they contain a wealth of information, most of which we have yet to fully comprehend. For example, the phantom presents us with information gathered from such research as the Cape Roberts project and deep ice core drilling, which reveal past climatic events from which to gauge current and possible future trends. According to Tim Naish, of the Imstitute of Geological and Nuclear Sciences: "We have moved from a phase of scientific exploration to one of realisation that much of what we are learning about Antarctica and the Southern Ocean has major implications for understanding the past and future of our planet" (Naish, 1999)

A principled approach toward symbolic geometric constraint satisfaction

An important problem in geometric reasoning is to find the configuration of a collection of geometric bodies so as to satisfy a set of given constraints. Recently, it has been suggested that this problem can be solved efficiently by symbolically reasoning about geometry. This approach, called degrees of freedom analysis, employs a set of specialized routines called plan fragments that specify how to change the configuration of a set of bodies to satisfy a new constraint while preserving existing constraints. A potential drawback, which limits the scalability of this approach, is concerned with the difficulty of writing plan fragments. In this paper we address this limitation by showing how these plan fragments can be automatically synthesized using first principles about geometric bodies, actions, and topology. 1

Integrating NEON data with existing models: An example with the Community Land Model

<p>The manuscript and accompanying talk for the  International Congress on Environmental Modeling and Software on building informatics infrastructure to support the integration of models and data.  Please cite as: "Hart, E.M., Fox, A., Berukoff, S., Hoar, T., 2014 Integrating NEON data with existing models: An example with the Community Land Model  In: Ames, D.P., Quinn, N.W.T., Rizzoli, A.E. (Eds.), Proceedings of the 7th International Congress on Environmental Modelling and Software, June 15-19, San Diego, California, USA. http://www.iemss.org/society/index.php/iemss-2014-proceedings"</p> <p> </p

Evaluation of a cosmic-ray neutron sensor network for improved land surface model predictio

In-situ soil moisture sensors provide highly accurate but very local soil moisture measurements while remotely sensed soil moisture is strongly affected by vegetation and surface roughness. In contrast, Cosmic-Ray Neutron Sensors (CRNS) allow highly accurate soil moisture estimation at the field scale which could be valuable to improve land surface model predictions. In this study, the potential of a network of CRNS installed in the 2354 km2 Rur catchment (Germany) for estimating soil hydraulic parameters and improving soil moisture states was tested. Data measured by the CRNS were assimilated with the local ensemble transform Kalman filter in the Community Land Model v. 4.5. Data of four, eight and nine CRNS were assimilated for the years 2011 and 2012 (with and without soil hydraulic parameter estimation), followed by a verification year 2013 without data assimilation. This was done using (i) a regional high resolution soil map, (ii) the FAO soil map and (iii) an erroneous, biased soil map as input information for the simulations. For the regional soil map, soil moisture characterization was only improved in the assimilation period but not in the verification period. For the FAO soil map and the biased soil map soil moisture predictions improved strongly to a root mean square error of 0.03 cm3/cm3 for the assimilation period and 0.05 cm3/cm3 for the evaluation period. Improvements were limited by the measurement error of CRNS (0.03 cm3/cm3). The positive results obtained with data assimilation of nine CRNS were confirmed by the jackknife experiments with four and eight CRNS used for assimilation. The results demonstrate that assimilated data of a CRNS network can improve the characterization of soil moisture content at the catchment scale by updating spatially distributed soil hydraulic parameters of a land surface model

Uncertainty in Terrestrial Water Cycle Simulations

TerrSysMP has been exploited to advance our understanding of terrestrial water cycle, by conducting km-scale simulations from field scale to continental scale at the massively parallel supercomputing environment of the Jülich Supercomputing Centre (JSC). The numerical simulations have led to quantification of uncertainties in the simulated terrestrial water cycle in terms of grid-scale representation of heterogeneity and bio-geophysical parameterisations. Ensemble simulations are thus prerequisite to quantify the uncertainty in the terrestrial water cycle, which then could also be utilised for data assimilation to improve prediction

Assimilation of MODIS Snow Cover Through the Data Assimilation Research Testbed and the Community Land Model Version 4

To improve snowpack estimates in Community Land Model version 4 (CLM4), the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) was assimilated into the Community Land Model version 4 (CLM4) via the Data Assimilation Research Testbed (DART). The interface between CLM4 and DART is a flexible, extensible approach to land surface data assimilation. This data assimilation system has a large ensemble (80-member) atmospheric forcing that facilitates ensemble-based land data assimilation. We use 40 randomly chosen forcing members to drive 40 CLM members as a compromise between computational cost and the data assimilation performance. The localization distance, a parameter in DART, was tuned to optimize the data assimilation performance at the global scale. Snow water equivalent (SWE) and snow depth are adjusted via the ensemble adjustment Kalman filter, particularly in regions with large SCF variability. The root-mean-square error of the forecast SCF against MODIS SCF is largely reduced. In DJF (December-January-February), the discrepancy between MODIS and CLM4 is broadly ameliorated in the lower-middle latitudes (2345N). Only minimal modifications are made in the higher-middle (4566N) and high latitudes, part of which is due to the agreement between model and observation when snow cover is nearly 100. In some regions it also reveals that CLM4-modeled snow cover lacks heterogeneous features compared to MODIS. In MAM (March-April-May), adjustments to snowmove poleward mainly due to the northward movement of the snowline (i.e., where largest SCF uncertainty is and SCF assimilation has the greatest impact). The effectiveness of data assimilation also varies with vegetation types, with mixed performance over forest regions and consistently good performance over grass, which can partly be explained by the linearity of the relationship between SCF and SWE in the model ensembles. The updated snow depth was compared to the Canadian Meteorological Center (CMC) data. Differences between CMC and CLM4 are generally reduced in densely monitored regions

Observing System Simulation Experiments in the Marmara Sea

Poster presented in Symposium for the legacy of Anna Trevisan in Bologna in Oct 201