Micrometeorological Data Set Research Articles

Data Filling of Micrometeorological Variables in Complex Terrain for High-Resolution Nowcasting

In this paper, two different computationally inexpensive methods for nowcasting/data filling spatially varying meteorological variables (wind velocity components, specific humidity, and virtual potential temperature) covering scales ranging from 100 m to 5 km in regions marked by complex terrain are compared. Multivariable linear regression and artificial neural networks are used to predict micrometeorological variables at eight locations using the measurements from three nearby weather stations. The models are trained using data gathered from a system of eleven low-cost automated weather stations that were deployed in the Cadarache Valley of southeastern France from December 2016 to June 2017. The models are tested on two held-out periods of measurements of thermally-driven flow and synoptically forced flow. It is found that the models have statistically significant performance differences for the wind components during the synoptically driven flow period (p = 6.6 × 10−3 and p = 2.0 × 10−2 for U and V, respectively), but perform the same otherwise. These methods can be used to spatially fill gaps in micrometeorological datasets. Recommended future work should include statistically interpreting the predictive models and testing their capabilities on meteorological datasets from different locations.

A catchment water balance assessment of an abrupt shift in evapotranspiration at the Hubbard Brook Experimental Forest, New Hampshire, USA

AbstractSmall catchments have served as sentinels of forest ecosystem responses to changes in air quality and climate. The Hubbard Brook Experimental Forest in New Hampshire has been tracking catchment water budgets and their controls – meteorology and vegetation – since 1956. Water budgets in four reference catchments indicated an approximately 30% increase in the evapotranspiration (ET) as estimated by the difference between precipitation (P) and runoff (RO) starting in 2010 and continuing through 2019. We analyzed the annual water budgets, cumulative deviations of the daily P, RO and water budget residual (WBR = P − RO), potential ET (PET) and indicators of subsurface storage to gain greater insight into this shift in the water budgets. The PET and the subsurface storage indicators suggest that this change in WBR was primarily due to increasing ET. While multiple long‐term hydrological and micrometeorological data sets were used to detect and investigate this change in ET, additional measurements of groundwater storage and soil moisture would enable better estimation of ET within the catchment water balance. Increasing the breadth of long‐term measurements across small gauged catchments allows them to serve as more effective sentinels of substantial hydrologic changes like the ET increase that we observed.

Environmental controls on ecosystem-scale cold-season methane and carbon dioxide fluxes in an Arctic tundra ecosystem

Abstract. Understanding the processes that influence and control carbon cycling in Arctic tundra ecosystems is essential for making accurate predictions about what role these ecosystems will play in potential future climate change scenarios. Particularly, air–surface fluxes of methane and carbon dioxide are of interest as recent observations suggest that the vast stores of soil carbon found in the Arctic tundra are becoming more available to release to the atmosphere in the form of these greenhouse gases. Further, harsh wintertime conditions and complex logistics have limited the number of year-round and cold-season studies and hence too our understanding of carbon cycle processes during these periods. We present here a two-year micrometeorological data set of methane and carbon dioxide fluxes, along with supporting soil pore gas profiles, that provide near-continuous data throughout the active summer and cold winter seasons. Net emission of methane and carbon dioxide in one of the study years totalled 3.7 and 89 g C m−2 a−1 respectively, with cold-season methane emission representing 54 % of the annual total. In the other year, net emission totals of methane and carbon dioxide were 4.9 and 485 g C m−2 a−1 respectively, with cold-season methane emission here representing 82 % of the annual total – a larger proportion than has been previously reported in the Arctic tundra. Regression tree analysis suggests that, due to relatively warmer air temperatures and deeper snow depths, deeper soil horizons – where most microbial methanogenic activity takes place – remained warm enough to maintain efficient methane production whilst surface soil temperatures were simultaneously cold enough to limit microbial methanotrophic activity. These results provide valuable insight into how a changing Arctic climate may impact methane emission, and highlight a need to focus on soil temperatures throughout the entire active soil profile, rather than rely on air temperature as a proxy for modelling temperature–methane flux dynamics.

A Method for Identifying Kolmogorov’s Inertial Subrange in the Velocity Variance Spectrum

AbstractKolmogorov’s inertial subrange is one of the most recognized concepts in fluid turbulence. However, the practical application of this theory to turbulent flows requires identifying subrange bandwidth. In the atmospheric boundary layer, decades of investigation support Kolmogorov’s theory, but the techniques used to identify the subrange vary and no systematic approach has emerged. The algorithm for robust identification of the inertial subrange (ARIIS) has been developed to facilitate empirical studies of the turbulence cascade. ARIIS systematically and robustly identifies the most probable subrange bandwidth in a given velocity variance spectrum. The algorithm is a novel approach in that it directly uses the expected 3/4 ratio between streamwise and transverse velocity components to locate the onset and extent of the inertial subrange within a single energy density spectrum. Furthermore, ARIIS does not assume a −5/3 power law but instead uses a robust, iterative statistical fitting technique to derive the slope over the identified range. This algorithm was tested using a comprehensive micrometeorological dataset obtained from the Floating Instrument Platform (FLIP). The analysis revealed substantial variation in the inertial subrange bandwidth and spectral slope, which may be driven, in part, by mechanical wind–wave interactions. Although demonstrated using marine atmospheric data, ARIIS is a general approach that can be used to study the energy cascade in other turbulent flows.

Hydrometeorological measurements in peatland‐dominated, discontinuous permafrost at Scotty Creek, Northwest Territories, Canada

AbstractThe discontinuous permafrost region of northwestern Canada is experiencing rapid warming resulting in dramatic land cover change from forested peatland permafrost terrain to treeless wetlands. Extensive research has been conducted throughout this region to gain insight into how climate‐induced land cover change will impact water resources and ecosystem function. This paper presents a hydrological and micrometeorological dataset collected in the Scotty Creek basin, Northwest Territories, Canada over the period of 01 October 2014 to 30 September 2015, a sample of the intensive and coordinated measurements collected annually at this site. Micrometeorological data collected from four stations, one located in each of the land cover types representative of those comprising the Scotty Creek basin including bog, channel fen, stable peat plateau and peat plateau undergoing rapid permafrost degradation and loss, are presented. Monitored micrometeorological variables include incoming and outgoing shortwave and longwave radiation, air temperature, relative humidity, wind speed, precipitation (rain and snow) and snow depth. Deep ground temperatures (~1–10 m below the ground surface) from a channel fen as well as disturbed sites common to the basin including a seismic line and winter road are presented. Water levels were also monitored in the representative land cover types over this period. This dataset is available from the Wilfrid Laurier University Library Research Data Repository (https://doi.org/10.5683/SP/OQDRJG) and can be used in coordination with other hydrological and micrometeorological datasets to examine spatio‐temporal effects of meteorological conditions on local hydrological responses across cold regions.

A comparative analysis of micrometeorological determinants of evapotranspiration rates within a heterogeneous urban environment

Variability in micrometeorological conditions and their influence on estimated reference evapotranspiration (RET) rates were evaluated across a heterogeneous urban environment. Micrometeorological data sets (incoming solar radiation, air temperature, relative humidity and wind speed) were collected over a one-year period at six weather stations in New York City, NY (USA). Weather stations are located at four new urban green space monitoring sites and two airports. Reference evapotranspiration (RET) rates were estimated from the micrometeorological data sets for a short reference surface at a daily time-step using the ASCE Standardized Reference Evapotranspiration Equation, a Penman-Monteith based combination equation. Non-parametric comparative statistical analyses (Kruskal-Wallis) revealed statistically significant differences (at significance level α = 0.05) in micrometeorological conditions and estimated RET rates between the six sites. On a cumulative annual basis, estimated RET varied by up to 40 percent between the sites. A new technique for adjusting weather data collected at one location (e.g. regional airports) for use at another location (e.g. interior engineered urban green spaces) was evaluated. The study highlights the importance, for accurate estimation of ET, of onsite micrometeorological data sets, but concludes that additional research is needed to more thoroughly characterize micrometeorological variability across heterogeneous urban environments, and also to evaluate the influence of non-meteorological determinants, e.g. vegetation type, soil/media type, media moisture conditions and anthropogenic heat fluxes, on urban ET.

Surface ablation model evaluation on a drifting ice island in the Canadian Arctic

A 4-week micro-meteorological dataset was collected by an automatic weather station on a small ice island (0.13km2) adrift off Bylot Island (Lancaster Sound, Nunavut, Canada) during the 2011 melt season. This dataset provided an opportunity to identify the environmental variables and energy fluxes that contribute most to surface ablation during the melt season, as well as test previously developed surface melt (ablation) models. Surface ablation was estimated using energy fluxes calculated using the bulk aerodynamic approach (EBAWS) and three existing surface ablation models. These models included a simple solar radiation model developed for iceberg use (CIS-IB), a more sophisticated energy-balance model developed for ice island use (CIS-II), and a temperature index melt (TIM) model based on an assumed relationship between air temperature, time, and surface ablation. The models were driven by our measured micro-meteorological data (optimal forcing) or regional environmental forecast data from the Global Environmental Multiscale (GEM) Model, which is used for operational iceberg modeling. The sensible heat flux contributed most to the ice surface's available melt energy (47%), followed by net radiation (38%) and the latent heat flux (30%), while the subsurface heat flux removed 15% of available energy. When cumulative surface ablation was predicted with these calculated energy fluxes (EBAWS), observed surface ablation was under-predicted by 38%. Results illustrate the decreased performance of the melt models when run with GEM data versus in-situ micro-meteorological data, which is optimal for model input but not available for operational modeling. The CIS-II model under-predicted cumulative surface ablation by 5.7% (RMSE=1.2cm) with observed micro-meteorological data and over-predicted cumulative surface ablation by 35% when run with GEM model data. This is likely a result of the GEM model wind speed being 57% greater than that recorded on the ice island. Since surface ablation plays a greater relative role in overall deterioration of ice islands than traditional icebergs due to morphological differences (size, surface structure), it must be accurately represented in operational ice island deterioration models. The costs and benefits between parsimonious TIM models and skilled energy-balance models are weighed here for operational modelers to consider, along with the complications caused by the use of the regional environmental data input provided by the GEM model for operational modeling efforts.

Delineation of surface energy exchanges variations during thunderstorm and non-thunderstorm days during pre-monsoon season

Turbulent surface energy exchanges from land–air interface play an important role in generation and enhancing the convection. During pre-monsoon months (April–May) thunderstorms generate over Chota Nagpur plateau area of NE India and move over the study area Ranchi (23°25′N, 85°26′E). Even though convective conditions prevail over Ranchi during these months, thunderstorm occurs on some days only. To address this important aspect, present study focuses on understanding of surface energy budget (SEB): partitioning of net solar radiation flux (QN) into sensible (QH), latent heat (QE) and soil heat fluxes during different epochs of thunderstorm activity over study site at Ranchi. For this purpose, micro-meteorological data sets which comprise of fast response turbulent measurements and slow response data during 2008, 2009 and 2010 over Ranchi are used. A total of 25 thunderstorm cases are selected for the present study. The study reveals that prior to the occurrence of thunderstorm the QH and QE fluxes reach the same order followed by soil heat flux (QG). No significant variation of soil heat (QG) flux is noticed between thunderstorm days and non-thunderstorm days. The variations in the partitioning of QN flux into QH, QE and QG fluxes are distinguishable between the days of thunderstorm (TD) and non-thunderstorm (NTD). This variation can be used as precursor signal for the occurrence of thunderstorm activity. The results emanated from the present work are important in validating the performance of the meso-scale models in simulating these storms.

Interpolating Sparsely Corrupted Signals in Micrometeorology

In real applications where data acquisition is carried out under extreme conditions, post-processing techniques for systematic corrections are of critical importance. In micrometeorological studies, it is often the case that acquired data contains both missing information and impulse noise due to instrumentation failure, data transmission and data rejection for quality assurance. In this work, we propose a simple algorithm based on an $\ell_{1}-\ell_{1}$ variational formulation that simultaneously suppresses impulse noise and interpolates missing information. Our approach consists of relaxing the objective function in the variational formulation with a strictly convex and continuously differentiable function that depends on a regularization parameter. We solve a sequence of strictly convex optimization subproblems as the regularization parameter goes to zero, converging to the solution of the original problem. Numerical experiments on real micrometeorological datasets are conducted showing the effectiveness of the proposed approach. Furthermore, a convergence analysis is presented providing theoretical guaranties of our method.

Hydrothermal regimes of the dry active layer

Evaporation and condensation in the soil column clearly influence year‐round nonconductive heat transfer dynamics in the dry active layer underlying semiarid permafrost regions. We deduced this from heat flux components quantified using state‐of‐the‐art micrometeorological data sets obtained in dry and moist summers and in winters with various snow cover depths. Vapor moves easily through large pores, some of which connect to the atmosphere, allowing (1) considerable active layer warming driven by pipe‐like snowmelt infiltration, and (2) direct vapor linkage between atmosphere and deeper soils. Because of strong adhesive forces, water in the dry active layer evaporates with great difficulty. The fraction of latent heat to total soil heat storage ranged from 26 to 45% in dry and moist summers, respectively. These values are not negligible, despite being smaller than those of arctic wet active layer, in which only freezing and thawing were considered.

Revisiting bulk heat transfer on Peyto Glacier, Alberta, Canada, in light of the OG parameterization

AbstractA scheme for katabatic turbulent heat transfer proposed by Oerlemans and Grisogono (2002), here referred to as the OG parameterization, is compared with bulk heat-transfer estimates on Peyto Glacier, Alberta, Canada. Automatic weather stations (AWSs) provide off-glacier data to drive the parameterization and glacier data for bulk estimates. Micrometeorological datasets are used to assess two schemes that employ the Monin-Obukhov stability parameter, z/L, to modify logarithmic, or neutral, bulk heat-transfer equations to allow for stability. Both schemes fail at >1 m above the surface, where the AWS sensors are located, unless a modified approach is used in which the stability correction is constant for z/L ≥1/3. Then the bulk sensible-heat-flux density falls to ≈0.93 of its neutral estimate at all measurement levels, thus providing a basis for comparison with the parameterization. The results of the comparison are very good, indicating that a one-to-one relationship between bulk and parameterized values can be achieved by optimizing the fit with a background exchange coefficient and, because there is only one off-glacier AWS, using a sinusoidal function to model the diurnal variation of the potential temperature lapse rate.

The Priestley-Taylor parameter α for the Amazon forest

Priestley and Taylor provided a practical formulation of the partitioning of net radiation between heat flux and evaporation contained within a parameter α. Their model (PTM) needs verification under a range of environmental conditions. Micrometeorological data sets collected over the Amazon forest at the Ducke Reserve site (2°57′S; 59°57′W) gave an opportunity to evaluate α. Evidence presented here and by others shows that there is pronounced diurnal variation in α, with minimum values around midday and maximum values in the morning and evening hours. During unstable and stable conditions in the daylight hours, the Bowen ratio ( B) varied from 0.10 to 0.57 and −0.71 to −0.08, respectively, whereas α varied from 0.67 to 1.16 and 1.28 to 3.12, respectively. A mean value of α = 1.16±0.56 was obtained from daytime hourly values for two days. The daily data sets from three expeditions gave a mean of α = 1.03±0.13. This work confirms that α is a function of atmospheric stability over the Amazon forest. Thus the PTM should be applied with caution over time-intervals of one day or less because of the sensitivity to variation in α. The calculated values of α are in general agreement with those reported in literature.