Assimilating MODIS and AMSR-E Snow Observations in a Snow Evolution Model

In this paper four simple computationally inexpensive, direct insertion data assimilation schemes are presented, and evaluated, to assimilate Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover, which is a binary observation, and Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E) snow water equivalent (SWE) observations, which are at a coarser resolution than MODIS, into a numerical snow evolution model. The four schemes are 1) assimilate MODIS snow cover on its own with an arbitrary 0.01 m added to the model cells if there is a difference in snow cover; 2) iteratively change the model SWE values to match the AMSR-E equivalent value; 3) AMSR-E scheme with MODIS observations constraining which cells can be changed, when both sets of observations are available; and 4) MODIS-only scheme when the AMSR-E observations are not available, otherwise scheme 3. These schemes are used in the winter of 2006/07 over the southeast corner of Colorado and the tri-state area: Wyoming, Colorado, and Nebraska. It is shown that the inclusion of MODIS data enables the model in the north domain to have a 15% improvement in number of days with a less than 10% disagreement with the MODIS observation 24 h later and approximately 5% for the south domain. It is shown that the AMSR-E scheme has more of an impact in the south domain than the north domain. The assimilation results are also compared to station snow-depth data in both domains, where there is up-to-a-factor-of-5 underestimation of snow depth by the assimilation schemes compared with the station data but the snow evolution is fairly consistent

Lidar and Deep Learning Reveal Forest Structural Controls on Snowpack

Forest structure has a strong relationship with abiotic components of the environment. For example, canopy morphology controls snow depth through interception and modifies incoming thermal radiation. In turn, snow water availability affects forest growth, carbon sequestration, and nutrient cycling. We investigated how structural diversity and topography affect snow depth patterns across scales. The study site, Grand Mesa, Colorado, is representative of many areas worldwide where declining snowpack and its consequences for forest ecosystems are increasingly an environmental concern. On the basis of a convolution neural network model (R2 of 0.64; root mean squared error of 0.13 m), we found that forest structural and topographic metrics from airborne light detection and ranging (lidar) at fine scales significantly influence snow depth during the accumulation season. Moreover, complex vertically arranged foliage intercepts more snow and results in shallower snow depths below the canopy. Assessing forest structural controls on snow distribution and depth will aid efforts to improve understanding of the ecological and hydrological impacts of changing snow patterns

Subgrid snow depth coefficient of variation spanning alpine to sub-alpine mountainous terrain

Given the substantial variability of snow in complex mountainous terrain, a considerable challenge of coarse scale modeling applications is accurately representing the subgrid variability of snowpack properties. The snow depth coefficient of variation (CVds) is a useful metric for characterizing subgrid snow distributions but has not been well defined by a parameterization for mountainous environments. This study utilizes lidar-derived snow depth datasets spanning alpine to sub-alpine mountainous terrain in Colorado, USA to evaluate the variability of subgrid snow distributions within a grid size comparable to a 1000 m resolution common for hydrologic and land surface models. The subgrid CVds exhibited a wide range of variability across the 321 km2 study area (0.15 to 2.74) and was significantly greater in alpine areas compared to subalpine areas. Mean snow depth was the dominant driver of CVds variability in both alpine and subalpine areas, as CVds decreased nonlinearly with increasing snow depths. This negative correlation is attributed to the static size of roughness elements (topography and canopy) that strongly influence seasonal snow variability. Subgrid CVds was also strongly related to topography and forest variables; important drivers of CVds included the subgrid variability of terrain exposure to wind in alpine areas and the mean and variability of forest metrics in subalpine areas. Two statistical models were developed (alpine and subalpine) for predicting subgrid CVds that show reasonable performance statistics. The methodology presented here can be used for characterizing the variability of CVds in snow-dominated mountainous regions, and highlights the utility of using lidar-derived snow datasets for improving model representations of snow processes

Coal tar creosote abuse by vapour inhalation presenting with renal impairment and neurotoxicity: a case report

A 56 year old aromatherapist presented with advanced renal failure following chronic coal tar creosote vapour inhalation, and a chronic tubulo-interstitial nephritis was identified on renal biopsy. Following dialysis dependence occult inhalation continued, resulting in seizures, ataxia, cognitive impairment and marked generalised cerebral atrophy. We describe for the first time a case of creosote abuse by chronic vapour inhalation, resulting in significant morbidity. Use of the polycyclic aromatic hydrocarbon-containing wood preservative coal tar creosote is restricted by many countries due to concerns over environmental contamination and carcinogenicity. This case demonstrates additional toxicities not previously reported with coal tar creosote, and emphasizes the health risks of polycyclic aromatic hydrocarbon exposure

Genome-wide association analysis identifies six new loci associated with forced vital capacity

Forced vital capacity (FVC), a spirometric measure of pulmonary function, reflects lung volume and is used to diagnose and monitor lung diseases. We performed genome-wide association study meta-analysis of FVC in 52,253 individuals from 26 studies and followed up the top associations in 32,917 additional individuals of European ancestry. We found six new regions associated at genome-wide significance (P < 5 × 10−8) with FVC in or near EFEMP1, BMP6, MIR-129-2/HSD17B12, PRDM11, WWOX, and KCNJ2. Two (GSTCD and PTCH1) loci previously associated with spirometric measures were related to FVC. Newly implicated regions were followed-up in samples of African American, Korean, Chinese, and Hispanic individuals. We detected transcripts for all six newly implicated genes in human lung tissue. The new loci may inform mechanisms involved in lung development and pathogenesis of restrictive lung disease

Association of Forced Vital Capacity with the Developmental Gene <i>NCOR2</i>

Background Forced Vital Capacity (FVC) is an important predictor of all-cause mortality in the absence of chronic respiratory conditions. Epidemiological evidence highlights the role of early life factors on adult FVC, pointing to environmental exposures and genes affecting lung development as risk factors for low FVC later in life. Although highly heritable, a small number of genes have been found associated with FVC, and we aimed at identifying further genetic variants by focusing on lung development genes. Methods Per-allele effects of 24,728 SNPs in 403 genes involved in lung development were tested in 7,749 adults from three studies (NFBC1966, ECRHS, EGEA). The most significant SNP for the top 25 genes was followed-up in 46,103 adults (CHARGE and SpiroMeta consortia) and 5,062 chi

Genome-wide association analysis identifies six new loci associated with forced vital capacity

Forced vital capacity (FVC), a spirometric measure of pulmonary function, reflects lung volume and is used to diagnose and monitor lung diseases. We performed genome-wide association study meta-analysis of FVC in 52,253 individuals from 26 studies and followed up the top associations in 32,917 additional individuals of European ancestry. We found six new regions associated at genome-wide significance (P < 5 × 10−8) with FVC in or near EFEMP1, BMP6, MIR129-2–HSD17B12, PRDM11, WWOX and KCNJ2. Two loci previously associated with spirometric measures (GSTCD and PTCH1) were related to FVC. Newly implicated regions were followed up in samples from African-American, Korean, Chinese and Hispanic individuals. We detected transcripts for all six newly implicated genes in human lung tissue. The new loci may inform mechanisms involved in lung development and the pathogenesis of restrictive lung disease

Abstracts from the NIHR INVOLVE Conference 2017

n/

Assimilating MODIS and AMSR-E Snow Observations in a Snow Evolution Model

In this paper four simple computationally inexpensive, direct insertion data assimilation schemes are presented, and evaluated, to assimilate Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover, which is a binary observation, and Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E) snow water equivalent (SWE) observations, which are at a coarser resolution than MODIS, into a numerical snow evolution model. The four schemes are 1) assimilate MODIS snow cover on its own with an arbitrary 0.01 m added to the model cells if there is a difference in snow cover; 2) iteratively change the model SWE values to match the AMSR-E equivalent value; 3) AMSR-E scheme with MODIS observations constraining which cells can be changed, when both sets of observations are available; and 4) MODIS-only scheme when the AMSR-E observations are not available, otherwise scheme 3. These schemes are used in the winter of 2006/07 over the southeast corner of Colorado and the tri-state area: Wyoming, Colorado, and Nebraska. It is shown that the inclusion of MODIS data enables the model in the north domain to have a 15% improvement in number of days with a less than 10% disagreement with the MODIS observation 24 h later and approximately 5% for the south domain. It is shown that the AMSR-E scheme has more of an impact in the south domain than the north domain. The assimilation results are also compared to station snow-depth data in both domains, where there is up-to-a-factor-of-5 underestimation of snow depth by the assimilation schemes compared with the station data but the snow evolution is fairly consistent