Weather Station Measurements Research Articles

Cloud and precipitation properties from ground-based remote-sensing instruments in East Antarctica

Abstract. A new comprehensive cloud–precipitation–meteorological observatory has been established at Princess Elisabeth base, located in the escarpment zone of Dronning Maud Land (DML), East Antarctica. The observatory consists of a set of ground-based remote-sensing instruments (ceilometer, infrared pyrometer and vertically profiling precipitation radar) combined with automatic weather station measurements of near-surface meteorology, radiative fluxes, and snow height. In this paper, the observatory is presented and the potential for studying the evolution of clouds and precipitating systems is illustrated by case studies. It is shown that the synergetic use of the set of instruments allows for distinguishing ice, liquid-containing clouds and precipitating clouds, including some information on their vertical extent. In addition, wind-driven blowing snow events can be distinguished from deeper precipitating systems. Cloud properties largely affect the surface radiative fluxes, with liquid-containing clouds dominating the radiative impact. A statistical analysis of all measurements (in total 14 months mainly during summer–beginning of winter) indicates that these liquid-containing clouds occur during as much as 20% of the cloudy periods. The cloud occurrence shows a strong bimodal distribution with clear-sky conditions 51% of the time and complete overcast conditions 35% of the time. Snowfall occurred during 17% of the cloudy periods with a predominance of light precipitation and only rare events with snowfall >1 mm h−1 water equivalent (w.e.). Three of such intense snowfall events occurred during 2011 contributing to anomalously large annual surface mass balance (SMB). Large accumulation events (>10 mm w.e. day−1) during the radar-measurement period of 26 months were always associated with snowfall, but at the same time other snowfall events did not always lead to accumulation. The multiyear deployment of a precipitation radar in Antarctica allows for assessing the contribution of the snowfall to the local SMB and comparing it to the other SMB components. During 2012, snowfall rate was 110 ± 20 mm w.e. yr−1, from which surface and drifting snow sublimation removed together 23%. Given the measured yearly SMB of 52 ± 3 mm w.e., the residual term of 33 ± 21 mm w.e. yr−1 was attributed to the wind-driven snow erosion. In general, this promising set of robust instrumentation allows for improved insight into cloud and precipitation processes in Antarctica and can be easily deployed at other Antarctic stations.

Methodology to Forecast Road Surface Temperature with Principal Components Analysis and Partial Least-Square Regression: Application to an Urban Configuration

A forecast road surface temperature (RST) helps winter services to optimize costs and to reduce the deicers environmental impacts. Data from road weather information systems (RWIS) and thermal mapping are considered inputs for forecasting physical numerical models. Statistical models include many meteorological parameters along routes and provide a spatial approach. It is based on typical combinations resulting from treatment and analysis of a database from measurements of road weather stations or thermal mapping, easy, reliable, and cost effective to monitor RST, and many meteorological parameters. A forecast dedicated to road networks should combine both spatial and time forecasts needs. This study contributed to building a reliable RST forecast based on principal component analysis (PCA) and partial least-square (PLS) regression. An urban stretch with various weather conditions and seasons was monitored over several months to generate an appropriate number of samples. The study first consisted of the identification of its optimum number to establish a reliable forecast. A second aspect is aimed at comparing RST forecasts from PLS model to measurements. Comparison indicated a forecast over an urban stretch with up to 94% of values within ±1°C and over 80% within ±3°C.

An environmental data set for vector-borne disease modeling and epidemiology.

Understanding the environmental conditions of disease transmission is important in the study of vector-borne diseases. Low- and middle-income countries bear a significant portion of the disease burden; but data about weather conditions in those countries can be sparse and difficult to reconstruct. Here, we describe methods to assemble high-resolution gridded time series data sets of air temperature, relative humidity, land temperature, and rainfall for such areas; and we test these methods on the island of Madagascar. Air temperature and relative humidity were constructed using statistical interpolation of weather station measurements; the resulting median 95th percentile absolute errors were 2.75°C and 16.6%. Missing pixels from the MODIS11 remote sensing land temperature product were estimated using Fourier decomposition and time-series analysis; thus providing an alternative to the 8-day and 30-day aggregated products. The RFE 2.0 remote sensing rainfall estimator was characterized by comparing it with multiple interpolated rainfall products, and we observed significant differences in temporal and spatial heterogeneity relevant to vector-borne disease modeling.

Calibration of a surface mass balance model for global-scale applications

Abstract. Global applications of surface mass balance models have large uncertainties, as a result of poor climate input data and limited availability of mass balance measurements. This study addresses several possible consequences of these limitations for the modelled mass balance. This is done by applying a simple surface mass balance model that only requires air temperature and precipitation as input data, to glaciers in different regions. In contrast to other models used in global applications, this model separately calculates the contributions of net solar radiation and the temperature-dependent fluxes to the energy balance. We derive a relation for these temperature-dependent fluxes using automatic weather station (AWS) measurements from glaciers in different climates. With local, hourly input data, the model is well able to simulate the observed seasonal variations in the surface energy and mass balance at the AWS sites. Replacing the hourly local data by monthly gridded climate data removes summer snowfall and winter melt events and, hence, influences the modelled mass balance most on locations with a small seasonal temperature cycle. Modelled winter mass balance profiles are fitted to observations on 82 glaciers in different regions to determine representative values for the multiplication factor and vertical gradient of precipitation. For 75 of the 82 glaciers, the precipitation provided by the climate dataset has to be multiplied with a factor above unity; the median factor is 2.5. The vertical precipitation gradient ranges from negative to positive values, with more positive values for maritime glaciers and a median value of 1.5 mm a−1 m−1. With calibrated precipitation, the modelled annual mass balance gradient closely resembles the observations on the 82 glaciers, the absolute values are matched by adjusting either the incoming solar radiation, the temperature-dependent flux or the air temperature. The mass balance sensitivity to changes in temperature is particularly sensitive to the chosen calibration method. We additionally calculate the mass balance sensitivity to changes in incoming solar radiation, revealing that widely observed variations in irradiance can affect the mass balance by a magnitude comparable to a 1 °C change in temperature or a 10% change in precipitation.

Traceability and Online Publication of Weather Station Measurements of Temperature, Pressure, and Humidity

A project to enhance the quality of climate data has been running at the Italian Institute of Metrology (INRiM) since 2007. The focus of the project has been the installation and development of a weather station to monitor temperature, air humidity, and pressure; the collection and storage of the measurements; and the provision online to allow open-access. The project aims to improve the traceability of measurement through development of calibration and measurement protocols able to be applied to the wide variety and geographical spread of weather stations. The data collected have a short traceability to the national standards and a well-known uncertainty budget. In this work the project progress in terms of data collection and calibration of the weather station is reported. The instant measured values of the weather parameters have been published online, as is a complete database of the recordings, stored in daily, monthly, and annually collected data archives. This is the first example at the Italian national level of an archive of reliable climate data open to public access. A traceability study was carried out through calibration of the weather station instruments both in situ (over the course of one seven-hour period) and in the laboratory (under the full range of expected temperature, humidity, and pressure conditions). Significant differences in the results of the two calibrations are noted and implications for the provision of traceability discussed.

Description of atmospheric conditions at the Pierre Auger Observatory using the Global Data Assimilation System (GDAS)

Atmospheric conditions at the site of a cosmic ray observatory must be known for reconstructing observed extensive air showers. The Global Data Assimilation System (GDAS) is a global atmospheric model predicated on meteorological measurements and numerical weather predictions. GDAS provides altitude-dependent profiles of the main state variables of the atmosphere like temperature, pressure, and humidity. The original data and their application to the air shower reconstruction of the Pierre Auger Observatory are described. By comparisons with radiosonde and weather station measurements obtained on-site in Malargüe and averaged monthly models, the utility of the GDAS data is shown.

Automated Ice Inference and Monitoring on the Veterans’ Glass City Skyway Bridge

AbstractUnder certain weather conditions, the Veterans’ Glass City Skyway (VGCS) Bridge in Toledo, Ohio, accumulates ice on the stay sheaths in such a way that has led to large fragments falling into traffic lanes. To aid the Ohio Department of Transportation (ODOT) in their response to this, an automatic inference and monitoring system was implemented. This system utilizes existing weather station measurement data to infer possible events, alerting officials and providing tools to further assess the situation. The development of the decision, the algorithm, and implementation process are presented. The system was implemented in January 2011, and it warned of and monitored an icing event that occurred February 20–24, 2011. Ice fell shortly after the system indicated conditions were met for icefall. ODOT personnel used the ice inference system to assist them in managing the response to the icing event.

Programme for Monitoring of the Greenland Ice Sheet (PROMICE): first temperature and ablation records

The Greenland ice sheet is reacting to climate change. Yet, mass-budget estimates differ considerably, partly due to climatic variability and partly to uncertainties in the techniques of assessing mass change (IPCC 2007). Nevertheless, all recent estimates agree that the ice sheet is losing mass (e.g. 286 Gt/yr; Velicogna 2009) at an accelerating rate (Rignot et al. 2011). On top of this, the area with a negative mass budget is expanding rapidly (Khan et al. 2010). The mass loss is attributed equally to increases in both iceberg production and melting of the ice sheet (Van den Broeke et al. 2009). The increasing mass loss in recent years has caught public attention and given rise to concern worldwide due to its potential impact on sea level. In the light of this, the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) was initiated in 2007 (Ahlstrøm & PROMICE project team 2008), lead by the Geological Survey of Denmark and Greenland (GEUS). PROMICE undertakes surface mass-budget measurements using automatic weather stations, quantifies the mass loss by iceberg calving using remotely sensed data from satellites and airborne surveys and tracks changes in the extent of glaciers. In this paper, we focus on weather station measurements, which are crucial in calculating the energy exchange between the atmosphere and the ice sheet, and in validating model calculations of the surface mass budget. In particular, we present the observed temperatures and investigate how their high 2010 values affected ablation in southern Greenland

Modelling surface‐air‐temperature variation over complex terrain around abisko, swedish lapland: uncertainties of measurements and models at different scales

Zhenlin, Y., Hanna, E. and Callaghan, T. V., 2011: Modelling surface‐air‐temperature variation over complex terrain around Abisko, Swedish Lapland: uncertainties of measurements and models at different scales. Geografiska Annaler: Series A, Physical Geography, 93, 89–112. DOI: 10.1111/j.1468‐0459. 2011.00005.xMany ecological, physical and geographical processes affected by climate in the natural environment are scale‐dependent: determining surface‐air‐temperature distribution at a scale of tens to hundreds of metres can facilitate such research, which is currently hampered by the relative dearth of meteorological stations and complex surface temperature characteristics, particularly in mountain areas. Here we discuss both the couplings and mismatch of present climatological data at different scales, ranging from ∼50 m to 100 km, and provide a novel model of the surface‐air‐temperature distribution in topographically heterogeneous regions.First, a comparison of the large‐scale weather station measurements and gridded climate reanalysis (ERA‐40) data is used to define regional climatology in the Swedish sub‐Arctic and obtain the mesoscale temperature lapse rates. Second, combined with temperature measurements obtained from transects set among complex terrain, key microclimatic characteristics of the temperature distribution are identified, showing few temperature inversions when the wind speed exceeds 3 m s−1, while temperature inversions prevail during calm nights. Besides wind, there is a pronounced winter temperature stratification around the large Lake Torneträsk, and variations in topography are found to have a strong influence in shaping the microscale temperature pattern through their effect on solar radiation during summer.A monthly 50‐m scale temperature‐distribution (topoclimate) model is built based on the above findings, and model validation is conducted using further fieldwork measurements from different seasons. We present results of surface‐air‐temperature distribution for the Abisko region, and discuss how these results help reconcile the scale mismatch mentioned above.

Snow cover analysis in Emilia-Romagna

In this paper we present an operational chain developed in the Emilia-Romagna region (Italy) to monitor snow cover and snow water equivalent (SWE) over the area managed by the Regional Catchment Technical Service. Remote sensing data from medium resolution sensors (MODIS and AVHRR/3) are used as input for snow cover detection algorithms. Hourly weather station measurements are used as input for a snow melt and accumulation model in order to estimate the snow water equivalent. The model reliability is mainly related to the network density of heated rain gauges detecting snow precipitation. The products are disseminated as web bulletins and they are used in monitoring chain and alert for the Civil Protection Agency.

Temperature thresholds for degree‐day modelling of Greenland ice sheet melt rates

Degree‐day factors (DDFs) are calculated for the ice sheet ablation zone in southwest Greenland, using measurements of automatic weather stations and a regional atmospheric climate model. The rapid increase of DDFs for snow and ice towards higher elevations is caused by the increasing dominance of short daytime melting and nocturnal refreezing. This spatial inhomogeneity can be avoided by choosing a lower threshold for daily average 2 m air temperature (268 K instead of 273.15 K) for the degree‐day calculation.

Local-scale validation of the Surface Observation Gridding System with in situ weather observations in a semi-arid environment

Although the Surface Observation Gridding System (SOGS) provides spatially continuous models of meteorological conditions, little work has been done to validate SOGS data independently for site-specific research and, as a result, a single nearby weather station is commonly selected instead. This study sought to determine local-scale accuracy of SOGS data (1) by correlation with independent, in situ weather station measurements and (2) relative to a nearby weather station. Correlations between SOGS data and in situ weather observations and between in situ weather observations and a nearby weather station were examined in a semi-arid environment of southeastern Idaho over the 2006 growing season. The results indicate that both SOGS and nearby weather station data were significantly correlated with in situ weather station measurements. Although temperature correlations between in situ and the nearby weather station were slightly greater compared to SOGS, SOGS data were a better predictor of precipitation. This suggests that the use of a nearby weather station is appropriate for local temperature parameters but precipitation parameters are better estimated using SOGS data. Overall, the validation of the SOGS weather models agreed closely with independent, in situ weather measurements and, as a result, greater confidence can be placed in the accuracy of the productivity, biomass and global climate change models derived from these data.

Satellite Microwave Remote Sensing of Daily Land Surface Air Temperature Minima and Maxima From AMSR-E

We present an approach to retrieve daily minimum and maximum 2-m height air temperatures from 18.7, and 23.8 GHz H and V polarized brightness temperature from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) during the snow free season. The approach accounts, with minimal ancillary data, for vertically integrated atmospheric water vapor, and variable surface emissivity due to open water and vegetation. Retrieved temperatures were evaluated using Northern Hemisphere weather stations and independent satellite-based air temperatures from the Atmosphere Infrared Sounder and Advanced Microwave Sounding Unit (AIRS/AMSU; hereafter AIRS) sensors on Aqua. The retrieved temperatures are within 1.0 - 3.5 K of surface weather station measurements for vegetated locations, but uncertainty can exceed 4 K for desert and sparsely vegetated regions, mainly due to site to site biases. The AIRS and AMSR-E temperature retrievals generally agree more closely with one another than with weather stations and are generally within 1.0-2.8 K over vegetated regions, but with less agreement ( > 4 K ) over desert and mountainous regions. Additional useful information produced by our approach includes open water fraction, vegetation optical depth and atmospheric water vapor. The results of this study provide inputs for land surface models and a new approach for monitoring of land surface air temperatures with well quantified accuracy and precision.

Quantifying the water balance in a planar hillslope plot: Effects of measurement errors on flow prediction

We examine comprehensively the water balance of an experimental plot in Seattle, WA, USA to determine the magnitude of hydrologic measurement errors and their effects on flow prediction. The plot represents a fully contained planar hillslope with known size and boundary conditions. Precipitation input to the plot is measured with a series of rain gauges installed above the ground surface and buried with rims at the ground surface level. Output from the plot via subsurface flow is measured by continuously recording tipping buckets. Evapotranspiration ( ET ) output is characterized using weather station and energy budget measurements. Analyses of water balance measurements show that the best precipitation measurements come from buried rain gauges, particularly simple funnel collectors, whereas surface rain gauges under-report rain by an average of 8%. ET is the most uncertain component of the water balance, with land surface energy budget, Penman–Monteith equation, and a calibrated empirical equation predicting seasonal ET rates that differ from one another by up to 18%. Standard un-calibrated empirical ET equations prove unsuitable for the plot site. To evaluate how differences in measured precipitation and evapotranspiration affect flow prediction, we run continuous flow simulations with the physically-based variably saturated flow model, HYDRUS-2D, for four scenarios, each with a different combination of forcing data (precipitation and reference ET time series). Results show that biases in the atmospheric forcing data propagate into flow predictions, causing biases in discharge predictions of up to 22%. Some scenarios show an apparent mass balance that results from compensating errors in precipitation and ET . Simulations demonstrate that errors in forcing data are not easily discernable from model performance metrics and highlight the importance of analyzing the water balance at multiple time scales to identify sources of bias in water balance predictions.

Retrieval of snow grain size over Greenland from MODIS

This paper presents a new automatic algorithm to derive optical snow grain size at 1 km resolution using Moderate Resolution Imaging Spectroradiometer (MODIS) measurements. The retrieval is conceptually based on an analytical asymptotic radiative transfer model which predicts spectral bidirectional snow reflectance as a function of the grain size and ice absorption. The snow grains are modeled as fractal rather than spherical particles in order to account for their irregular shape. The analytical form of solution leads to an explicit and fast retrieval algorithm. The time series analysis of derived grain size shows a good sensitivity to snow melting and snow precipitation events. Pre-processing is performed by a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, which includes gridding MODIS data to 1 km resolution, water vapor retrieval, cloud masking and an atmospheric correction. MAIAC cloud mask is a new algorithm based on a time series of gridded MODIS measurements and an image-based rather than pixel-based processing. Extensive processing of MODIS TERRA data over Greenland shows a robust discrimination of clouds over bright snow and ice. Because in-situ grain size measurements over Greenland were not available at the time of this work, the validation was performed using data of Aoki et al. (Aoki, T., Hori, M., Motoyoshi, H., Tanikawa, T., Hachikubo, A., Sugiura, K., et al. (2007). ADEOS-II/GLI snow/ice products — Part II: Validation results using GLI and MODIS data. Remote Sensing of Environment, 111, 274–290) collected at Barrow (Alaska, USA), and Saroma, Abashiri and Nakashibetsu (Japan) in 2001–2005. The retrievals correlate well with measurements in the range of radii ~ 0.1–1 mm, although retrieved optical diameter may be about a factor of 1.5 lower than the physical measured diameter. As part of validation analysis for Greenland, the derived grain size from MODIS over selected sites in 2004 was compared to the microwave brightness temperature measurements of SSM/I radiometer which is sensitive to the amount of liquid water in the snowpack. The comparison showed a good qualitative agreement, with both datasets detecting two main periods of snowmelt. Additionally, MODIS grain size was compared with predictions of the snow model CROCUS driven by measurements of the automatic weather stations of the Greenland Climate Network. We found that the MODIS value is on average a factor of two smaller than CROCUS grain size. This result agrees with the direct validation analysis indicating that the snow reflectance model may need a “calibration” factor of ~ 1.5 for the retrieved grain size to match the physical snow grain size. Overall, the agreement between CROCUS and MODIS results was satisfactory, in particular before and during the first melting period in mid-June. Following detailed time series analysis of snow grain size for four permanent sites, the paper presents maps of this important parameter over the Greenland ice sheet for the March–September period of 2004.

Mesoscale Structure of Trade Wind Convection over Puerto Rico: Composite Observations and Numerical Simulation

We examine the mesoscale structure of the atmospheric boundary layer (ABL), low-level circulation, and trade wind convection over the sub-tropical island of Puerto Rico in mid-summer. Shallow afternoon thunderstorms are frequently seen over the western plains of the island. Observational data include automatic weather station measurements, radiosonde profiles, infrared satellite images, and mesoscale reanalysis data with a focus on the summer of 2006. Satellite microwave radar data (TRMM and CloudSat) indicate that island clouds typically extend just above the −20°C level during afternoon hours with reflectivity values reaching 50 dBz. A singular value decomposition of 3-hourly high resolution satellite rainfall maps reveals an island mode. From this a composite is constructed for a group of ten cases. With a Froude number ≈1 the trade winds pass over the mountains and standing vortices and gravity waves are trapped in the meandering wake. The Weather and Research Forecasting (WRF) model at 1-km resolution with 51 vertical layers is used to simulate the short-lived thunderstorms for two cases: 27 June and 20 July 2006. The model correctly locates the convective cells that develop between 1400 and 1700 LST. The shallow afternoon thunderstorms are triggered by surface heat fluxes, confluent sea breezes and a mountain wake. Recommendations for enhanced observations are given.

Daily reference evapotranspiration for California using satellite imagery and weather station measurement interpolation

Important water resources in California's agricultural and urban landscapes are at risk without more efficient management strategies. Improved monitoring can increase the efficiency of water use and mitigate these potential risks. The California Irrigation Management Information System (CIMIS) programme helps farmers, turf managers, and other resource managers develop water budgets that improve irrigation scheduling and monitor water stress. The CIMIS system is a repository of meteorological data collected at over 130 computerised weather stations. These are located at key agricultural and municipal sites throughout California and provide comprehensive, timely, weather data collected hourly and daily. In this article, the CIMIS sensor system is combined with hourly NOAA Geostationary Operational Environmental Satellite (GOES) visible satellite data to develop a methodology to extend reference evapotranspiration (ET0) station estimations to spatial daily ET0 maps of California. The maps are calculated on a (2 km)2 grid, a high spatial resolution compared with the density of CIMIS stations. The hourly GOES satellite images are used to estimate cloud cover, which are used in turn to modify clear sky radiation estimates. These are combined with interpolated CIMIS weather station meteorological data to satisfy the Penman–Monteith ET0 equation.

The Influence of Changes in Cloud Cover on Recent Surface Temperature Trends in the Arctic

Abstract A method is presented to assess the influence of changes in Arctic cloud cover on the surface temperature trend, allowing for a more robust diagnosis of causes for surface warming or cooling. Seasonal trends in satellite-derived Arctic surface temperature under clear-, cloudy-, and all-sky conditions are examined for the period 1982–2004. The satellite-derived trends are in good agreement with trends in the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis product and surface-based weather station measurements in the Arctic. Surface temperature trends under clear and cloudy conditions have patterns similar to the all-sky trends, though the magnitude of the trends under cloudy conditions is smaller than those under clear-sky conditions, illustrating the negative feedback of clouds on the surface temperature trends. The all-sky surface temperature trend is divided into two parts: the first part is a linear combination of the surface temperature trends under clear and cloudy conditions; the second part is caused by changes in cloud cover as a function of the clear–cloudy surface temperature difference. The relative importance of these two components is different in the four seasons, with the first part more important in spring, summer, and autumn, but with both parts being equally important in winter. The contribution of biases in satellite retrievals is also evaluated.

Surface characteristics at Dome A, Antarctica: first measurements and a guide to future ice-coring sites

AbstractAn assessment of the glaciological and meteorological characteristics of Dome A, the summit of the East Antarctic ice sheet, is made based on field investigations during the austral summer of 2004/05. Knowledge of these characteristics is critical for future international studies such as deep ice-core drilling. The assessment shows that: (1) Dome A is characterized by a very low 10m depth firn temperature, –58.3˚C (nearly 3˚C lower than at EPICA Dome C and 1˚C lower than at Vostok). (2) Automatic weather station (AWS) measurements of snow surface height and reference layers in a snow pit indicate the present-day snow accumulation rate at Dome A is within the range 1–3cmw.e. a–1. Densification models suggest a range of 1–2cmw.e. a–1. This is lower than at other sites along the ice divide of East Antarctica (IDEA). Annual layers at Dome A are thus potentially thinner than at other sites, so that a longer record is preserved in a given ice thickness. (3) The average wind speed observed at Dome A (<4ms–1) is lower than at other sites along IDEA. Together, these parameters, combined with radio-echo sounding data and information on the subglacial drainage distribution beneath Dome A, suggest Dome A as a candidate site for obtaining the oldest ice core.

Climate analysis with satellite versus weather station data

This paper compares how well satellite versus weather station measurements of climate predict agricultural performance in Brazil, India, and the United States. Although weather stations give accurate measures of ground conditions, they entail sporadic observations that require interpolation where observations are missing. In contrast, satellites have trouble measuring some ground phenomenon such as precipitation but they provide complete spatial coverage of various parameters over a landscape. The satellite temperature measurements slightly outperform the interpolated ground station data but the precipitation ground measurements generally outperform the satellite surface wetness index. In India, the surface wetness index outperforms station precipitation but this may be reflecting irrigation, not climate. The results suggest that satellites provide promising measures of temperature but that ground station data may still be preferred for measuring precipitation in rural settings.