Assessing the spatial and temporal variation in the skill of precipitation forecasts from an NWP model

It is becoming increasingly important to be able to verify the spatial accuracy of precipitation forecasts, especially with the advent of high-resolution numerical weather prediction (NWP) models. In this article, the fractions skill score (FSS) approach has been used to perform a scale-selective evaluation of precipitation forecasts during 2003 from the Met Office mesoscale model (12 km grid length). The investigation shows how skill varies with spatial scale, the scales over which the data assimilation (DA) adds most skill, and how the loss of that skill is dependent on both the spatial scale and the rainfall coverage being examined. Although these results come from a specific model, they demonstrate how this verification approach can provide a quantitative assessment of the spatial behaviour of new finer-resolution models and DA techniques.

A reprocessing for climate of sea surface temperature from the Along-Track Scanning Radiometers: basis in radiative transfer

We present new radiative transfer simulations to support determination of sea surface temperature (SST) from Along Track Scanning Radiometer (ATSR) imagery. The simulations are to be used within the ATSR Reprocessing for Climate project. The simulations are based on the “Reference Forward Model” line-by-line model linked with a sea surface emissivity model that accounts for wind speed and temperature, and with a discrete ordinates scattering model (DISORT). Input to the forward model is a revised atmospheric profile dataset, based on full resolution ERA-40, with a wider range of high-latitude profiles to address known retrieval biases in those regions. Analysis of the radiative impacts of atmospheric trace gases shows that geographical and temporal variation of N2O, CH4, HNO3, and CFC-11 and CFC-12 have effects of order 0.05, 0.2, 0.1 K on the 3.7, 11, 12 μm channels respectively. In addition several trace gases, neglected in previous studies, are included using fixed profiles contributing ~ 0.04 K to top-of-atmosphere BTs. Comparison against observations for ATSR2 and AATSR indicates that forward model biases have been reduced from 0.2 to 0.5 K for previous simulations to ~ 0.1 K.

Analytical solutions of a minimal model of species migration in a bounded domain

A minimal model of species migration is presented which takes the form of a parabolic equation with boundary conditions and initial data. Solutions to the differential problem are obtained that can be used to describe the small- and large-time evolution of a species distribution within a bounded domain. These expressions are compared with the results of numerical simulations and are found to be satisfactory within appropriate temporal regimes. The solutions presented can be used to describe existing observations of nematode distributions, can be used as the basis for further work on nematode migration, and may also be interpreted more generally.

Technical note: a simple procedure for removing temporal discontinuities in ERA-Interim upper stratospheric temperatures for use in nudged chemistry-climate model simulations

This note describes a simple procedure for removing unphysical temporal discontinuities in ERA-Interim upper stratospheric global mean temperatures in March 1985 and August 1998 that have arisen due to changes in satellite radiance data used in the assimilation. The derived temperature adjustments (offsets) are suitable for use in stratosphere-resolving chemistry-climate models that are nudged (relaxed) to ERA-Interim winds and temperatures. Simulations using a nudged version of the Canadian Middle Atmosphere Model (CMAM) show that the inclusion of the temperature adjustments produces temperature time series that are devoid of the large jumps in 1985 and 1998. Due to its strong temperature dependence, the simulated upper stratospheric ozone is also shown to vary smoothly in time, unlike in a nudged simulation without the adjustments where abrupt changes in ozone occur at the times of the temperature jumps. While the adjustments to the ERA-Interim temperatures remove significant artefacts in the nudged CMAM simulation, spurious transient effects that arise due to water vapour and persist for about 5 yr after the 1979 switch to ERA-Interim data are identified, underlining the need for caution when analysing trends in runs nudged to reanalyses.

The statistical downscaling model-decision centric (SDSM-DC): conceptual basis and applications

Regional climate downscaling has arrived at an important juncture. Some in the research community favour continued refinement and evaluation of downscaling techniques within a broader framework of uncertainty characterisation and reduction. Others are calling for smarter use of downscaling tools, accepting that conventional, scenario-led strategies for adaptation planning have limited utility in practice. This paper sets out the rationale and new functionality of the Decision Centric (DC) version of the Statistical DownScaling Model (SDSM-DC). This tool enables synthesis of plausible daily weather series, exotic variables (such as tidal surge), and climate change scenarios guided, not determined, by climate model output. Two worked examples are presented. The first shows how SDSM-DC can be used to reconstruct and in-fill missing records based on calibrated predictor-predictand relationships. Daily temperature and precipitation series from sites in Africa, Asia and North America are deliberately degraded to show that SDSM-DC can reconstitute lost data. The second demonstrates the application of the new scenario generator for stress testing a specific adaptation decision. SDSM-DC is used to generate daily precipitation scenarios to simulate winter flooding in the Boyne catchment, Ireland. This sensitivity analysis reveals the conditions under which existing precautionary allowances for climate change might be insufficient. We conclude by discussing the wider implications of the proposed approach and research opportunities presented by the new tool.

The first real-time worldwide ionospheric predictions network: an advance in support of spaceborne experimentation, on-line model validation, and space weather

We report on the first realtime ionospheric predictions network and its capabilities to ingest a global database and forecast F-layer characteristics and "in situ" electron densities along the track of an orbiting spacecraft. A global network of ionosonde stations reported around-the-clock observations of F-region heights and densities, and an on-line library of models provided forecasting capabilities. Each model was tested against the incoming data; relative accuracies were intercompared to determine the best overall fit to the prevailing conditions; and the best-fit model was used to predict ionospheric conditions on an orbit-to-orbit basis for the 12-hour period following a twice-daily model test and validation procedure. It was found that the best-fit model often provided averaged (i.e., climatologically-based) accuracies better than 5% in predicting the heights and critical frequencies of the F-region peaks in the latitudinal domain of the TSS-1R flight path. There was a sharp contrast however, in model-measurement comparisons involving predictions of actual, unaveraged, along-track densities at the 295 km orbital altitude of TSS-1R In this case, extrema in the first-principle models varied by as much as an order of magnitude in density predictions, and the best-fit models were found to disagree with the "in situ" observations of Ne by as much as 140%. The discrepancies are interpreted as a manifestation of difficulties in accurately and self-consistently modeling the external controls of solar and magnetospheric inputs and the spatial and temporal variabilities in electric fields, thermospheric winds, plasmaspheric fluxes, and chemistry.

Exploiting satellite-based rainfall for weather index insurance: the challenges of spatial and temporal aggregation

Lack of access to insurance exacerbates the impact of climate variability on smallholder famers in Africa. Unlike traditional insurance, which compensates proven agricultural losses, weather index insurance (WII) pays out in the event that a weather index is breached. In principle, WII could be provided to farmers throughout Africa. There are two data-related hurdles to this. First, most farmers do not live close enough to a rain gauge with sufficiently long record of observations. Second, mismatches between weather indices and yield may expose farmers to uncompensated losses, and insurers to unfair payouts – a phenomenon known as basis risk. In essence, basis risk results from complexities in the progression from meteorological drought (rainfall deficit) to agricultural drought (low soil moisture). In this study, we use a land-surface model to describe the transition from meteorological to agricultural drought. We demonstrate that spatial and temporal aggregation of rainfall results in a clearer link with soil moisture, and hence a reduction in basis risk. We then use an advanced statistical method to show how optimal aggregation of satellite-based rainfall estimates can reduce basis risk, enabling remotely sensed data to be utilized robustly for WII.

Temporal lobe epilepsy and social behavior: An animal model

Social behavior depends on the integrity of social brain circuitry. The temporal lobe is an important part of the social brain, and manifests morphological and functional alterations in autism spectrum disorders (ASD). Rats with temporal lobe epilepsy (TLE), induced with pilocarpine, were subjected to a social discrimination test that has been used to investigate potential animal models of ASD, and the results were compared with those for the control group. Rats with TLE exhibited fewer social behaviors than controls. No differences were observed in nonsocial behavior between groups. The results suggest an important role for the temporal lobe in regulating social behaviors. This animal model might be used to explore some questions about ASD pathophysiology. (c) 2008 Elsevier Inc. All rights reserved.

A Learning Function for Parameter Reduction in Spiking Neural Networks with Radial Basis Function

Spiking neural networks - networks that encode information in the timing of spikes - are arising as a new approach in the artificial neural networks paradigm, emergent from cognitive science. One of these new models is the pulsed neural network with radial basis function, a network able to store information in the axonal propagation delay of neurons. Learning algorithms have been proposed to this model looking for mapping input pulses into output pulses. Recently, a new method was proposed to encode constant data into a temporal sequence of spikes, stimulating deeper studies in order to establish abilities and frontiers of this new approach. However, a well known problem of this kind of network is the high number of free parameters - more that 15 - to be properly configured or tuned in order to allow network convergence. This work presents for the first time a new learning function for this network training that allow the automatic configuration of one of the key network parameters: the synaptic weight decreasing factor.

Temporal variability of soil CO2 emission after conventional and reduced tillage described by an exponential decay in time model

A quantificação do impacto das práticas de preparo sobre as perdas de carbono do solo é dependente da habilidade de se descrever a variabilidade temporal da emissão de CO2 do solo após preparo. Tem sido sugerido que as grandes quantidades de CO2 emitido após o preparo do solo podem servir como um indicador das modificações nos estoques de carbono do solo em longo termo. Neste trabalho é apresentado um modelo de duas partes baseado na temperatura e na umidade do solo e que inclui um termo exponencial decrescente do tempo que é eficiente no ajuste das emissões intermediárias após preparo: arado de disco seguido de uma passagem com a grade niveladora (convencional) e escarificador de arrasto seguido da passagem com rolo destorroador (reduzido). As emissões após o preparo do solo são descritas utilizando-se estimativa não linear com um coeficiente de determinação (R²) tão alto quanto 0.98 após preparo reduzido. Os resultados indicam que nas previsões da emissão de CO2 após o preparo do solo é importante considerar um termo exponencial decrescente no tempo após preparo.

Short-term temporal changes of soil carbon losses after tillage described by a first-order decay model

Tillage stimulates soil carbon (C) losses by increasing aeration, changing temperature and moisture conditions, and thus favoring microbial decomposition. In addition, soil aggregate disruption by tillage exposes once protected organic matter to decomposition. We propose a model to explain carbon dioxide (CO2) emission after tillage as a function of the no-till emission plus a correction due to the tillage disturbance. The model assumes that C in the readily decomposable organic matter follows a first-order reaction kinetics equation as: dC(sail)(t)/dt = -kC(soil)(t) and that soil C-CO2 emission is proportional to the C decay rate in soil, where C-soil(t) is the available labile soil C (g m(-2)) at any time (t). Emissions are modeled in terms soil C available to decomposition in the tilled and non-tilled plots, and a relationship is derived between no-till (F-NT) and tilled (F-Gamma) fluxes, which is: F-T = a1F(NT)e(-a2t), where t is time after tillage. Predicted and observed fluxes showed good agreement based on determination coefficient (R-2), index of agreement and model efficiency, with R-2 as high as 0.97. The two parameters included in the model are related to the difference between the decay constant (k factor) of tilled and no-till plots (a(2)) and also to the amount of labile carbon added to the readily decomposable soil organic matter due to tillage (a,). These two parameters were estimated in the model ranging from 1.27 and 2.60 (a(1)) and - 1.52 x 10(-2) and 2.2 x 10(-2) day(-1) (a(2)). The advantage is that temporal variability of tillage-induced emissions can be described by only one analytical function that includes the no-till emission plus an exponential term modulated by tillage and environmentally dependent parameters. (C) 2008 Elsevier B.V. All rights reserved.

CERN LHC signals for a neutrino mass model with bilinear R-parity violating minimal anomaly mediated supersymmetry

We investigate a neutrino mass model in which the neutrino data is accounted for by bilinear R-parity violating supersymmetry with anomaly mediated supersymmetry breaking. We focus on the CERN Large Hadron Collider (LHC) phenomenology, studying the reach of generic supersymmetry search channels with leptons, missing energy and jets. A special feature of this model is the existence of long-lived neutralinos and charginos which decay inside the detector leading to detached vertices. We demonstrate that the largest reach is obtained in the displaced vertices channel and that practically all of the reasonable parameter space will be covered with an integrated luminosity of 10 fb(-1). We also compare the displaced vertex reaches of the LHC and Tevatron.

Short-term temporal changes in the spatial variability model of CO2 emissions from a Brazilian bare soil

In this work, the spatial variability model of CO2 emissions and soil properties of a Brazilian bare soil were investigated. Carbon dioxide emissions were measured on three different days at contrasted soil temperature and soil moisture conditions, and soil properties were investigated at the same points where emissions were measured. One spatial variability model of soil CO2 emissions was found for each measurement day, and these models are similar to the ones of soil properties studied in an area of 100 x 100 m. (C) 2000 Elsevier B.V. Ltd. All rights reserved.

Spatial and temporal profiles for anti-inflammatory gene expression in leukocytes during a resolving model of peritonitis

The recent appreciation of the role played by endogenous counterregulatory mechanisms in controlling the outcome of the host inflammatory response requires specific analysis of their spatial and temporal profiles. In this study, we have focused on the glucocorticoid-regulated anti-inflammatory mediator annexin 1. Induction of peritonitis in wild-type mice rapidly (4 h) produced the expected signs of inflammation, including marked activation of resident cells (e.g., mast cells), migration of blood-borne leukocytes, mirrored by blood neutrophilia. These changes subsided after 48-96 h. In annexin 1null mice, the peritonitis response was exaggerated (∼40% at 4 h), with increased granulocyte migration and cytokine production. In blood leukocytes, annexin 1 gene expression was activated at 4, but not 24, h postzymosan, whereas protein levels were increased ai both time points. Locally, endothelial and mast cell annexin 1 gene expression was not detectable in basal conditions, whereas it was switched on during the inflammatory response. The significance of annexin 1 system plasticity in the anti-inflammatory properties of dexamethasone was assessed. Clear induction of annexin 1 gene in response to dexamethasone treatment was evident in the circulating and migrated leukocytes, and in connective tissue mast cells; this was associated with the steroid failure to inhibit leukocyte trafficking, cytokine synthesis, and mast cell degranulation in the annexin 1null mouse. In conclusion, understanding how inflammation is brought under control will help clarify the complex interplay between pro- and anti-inflammatory pathways operating during the host response to injury and infection. Copyright © 2006 by The American Association of Immunologists, Inc.