scholarly journals Comparing Kriging Estimators Using Weather Station Data and Local Greenhouse Sensors

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1853
Author(s):  
Pei-Fen Kuo ◽  
Tzu-En Huang ◽  
I Gede Brawiswa Putra
Keyword(s):  
Weather Conditions ◽  
Environmental Data ◽  
Weather Station ◽  
Weather Information ◽  
Station Data ◽  
Weather Station Data ◽  
Real Value ◽  
Weather Stations ◽  
Spatio Temporal ◽  
Central Weather Bureau

In order to minimize the impacts of climate change on various crops, farmers must learn to monitor environmental conditions accurately and effectively, especially for plants that are particularly sensitive to the weather. On-site sensors and weather stations are two common methods for collecting data and observing weather conditions. Although sensors are capable of collecting accurate weather information on-site, they can be costly and time-consuming to install and maintain. An alternative is to use the online weather stations, which are usually government-owned and free to the public; however, their accuracy is questionable because they are frequently located far from the farmers’ greenhouses. Therefore, we compared the accuracy of kriging estimators using the weather station data (collected by the Central Weather Bureau) to local sensors located in the greenhouse. The spatio-temporal kriging method was used to interpolate temperature data. The real value at the central point of the greenhouse was used for comparison. According to our results, the accuracy of the weather station estimator was slightly lower than that of the local sensor estimator. Farmers can obtain accurate estimators of environmental data by using on-site sensors; however, if they are unavailable, using a nearby weather station estimator is also acceptable.

scholarly journals Naturally triggered persistent deep slab avalanches in western Canada Part II: weather trends from model forecasts

2016 ◽  
Vol 62 (232) ◽  
pp. 256-269 ◽  
Author(s):  
MICHAEL CONLAN ◽  
BRUCE JAMIESON
Keyword(s):  
Classification Tree ◽  
Surface Wind ◽  
Critical Parameters ◽  
Wind Loading ◽  
Weather Data ◽  
Limited Area ◽  
Weather Station ◽  
Station Data ◽  
Weather Station Data ◽  
Weather Stations

ABSTRACTFor 175 difficult-to-forecast persistent deep slab avalanches, weather data were obtained from Global Environmental Multiscale (GEM) models produced by Environment Canada. The focus was to determine critical parameters and thresholds for avalanche forecasting from GEM and compare them with weather station data analyzed in Part I (Conlan and Jamieson, this issue). The high-resolution GEM-limited-area model (2.5 km resolution) forecasted higher median precipitation amounts than both the lower-resolution GEM15 (15 km resolution) and weather stations within a small dataset. Air temperatures were lower for both weather models compared with the weather station data, likely because of elevation differences. A multivariate classification tree created with GEM15 data correctly classified 29 of 36 avalanches by their primary cause-of-release, using a primary split of modelled solar warming of 5.9°C, 10 cm into the snowpack. For all 175 avalanches, GEM15 forecasted significantly less precipitation than observed at the weather stations, particularly with multi-day cumulative amounts. The majority of GEM15 surface wind speeds were between 0 and 10 km h−1, producing negligible wind loading amounts. The parameter values may be helpful for predicting future persistent deep slab avalanches. However, GEM output is not always representative of field conditions and should be used in conjunction with other sources.

scholarly journals PSIII-12 Genetic analysis of heat tolerance in Holsteins using test-day production records and satellite-based meteorological data

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 229-230
Author(s):  
Paige L Rockett ◽  
Flavio Schenkel ◽  
Christine F Baes ◽  
Filippo Miglior ◽  
Dan Tulpan
Keyword(s):  
Heat Stress ◽  
Dairy Cattle ◽  
Heat Tolerance ◽  
Meteorological Data ◽  
Genetic Selection ◽  
Weather Station ◽  
Station Data ◽  
Weather Station Data ◽  
Weather Stations ◽  
Selection For

Abstract Heat stress in dairy cattle is an existing issue in temperate regions that can cause reduced milk production, impaired fertility, and mortality. Genetic selection for heat tolerance using test-day production records and weather station data is a potential mitigation strategy. However, weather stations can have temporal data gaps and a low spatial resolution, which reduces the number of herds that can be incorporated into an analysis. The objectives for this study include: (1) compare satellite-based meteorological data from the NASA POWER database to weather station records in Ontario and Quebec, (2) evaluate the effects of heat stress on Canadian Holsteins, and (3) assess breeding value estimates for heat tolerance in the same population. Daily estimates of ambient temperature, dewpoint temperature, relative humidity, and wind speed from 481 weather stations in Ontario and Quebec were compared to the parameters estimated by the NASA POWER project using an ordinary least squares regression. The coordinates of herds in Ontario and Quebec were estimated using their addresses and Google Maps Geocoding. The best weather data for each herd location will be incorporated into two random regression animal models to analyze three test-day production traits: milk, fat, and protein yield. The first model will be used to estimate general and specific additive genetic merits over the thermal gradient. The second model will estimate the traditional additive genetic merit. In conclusion, this study explores the use of satellite estimated meteorological parameters in addition to or alternatively to weather station data in heat tolerance studies, quantifies the sensitivity of Canadian dairy cattle to heat stress, and evaluates if genetic selection for increased heat tolerance in Canadian dairy herds is possible.

scholarly journals Meteorological effects of the solar eclipse of 20 March 2015: analysis of UK Met Office automatic weather station data and comparison with automatic weather station data from the Faroes and Iceland

2016 ◽  
Vol 374 (2077) ◽  
pp. 20150212 ◽  
Author(s):  
Edward Hanna ◽  
John Penman ◽  
Trausti Jónsson ◽  
Grant R. Bigg ◽  
Halldór Björnsson ◽  
...  
Keyword(s):  
Wind Speed ◽  
Air Temperature ◽  
Solar Eclipse ◽  
Temperature Drop ◽  
Weather Station ◽  
Automatic Weather Station ◽  
Station Data ◽  
Weather Station Data ◽  
The Mean ◽  
Weather Stations

Here, we analyse high-frequency (1 min) surface air temperature, mean sea-level pressure (MSLP), wind speed and direction and cloud-cover data acquired during the solar eclipse of 20 March 2015 from 76 UK Met Office weather stations, and compare the results with those from 30 weather stations in the Faroe Islands and 148 stations in Iceland. There was a statistically significant mean UK temperature drop of 0.83±0.63°C, which occurred over 39 min on average, and the minimum temperature lagged the peak of the eclipse by about 10 min. For a subset of 14 (16) relatively clear (cloudy) stations, the mean temperature drop was 0.91±0.78 (0.31±0.40)°C but the mean temperature drops for relatively calm and windy stations were almost identical. Mean wind speed dropped significantly by 9% on average during the first half of the eclipse. There was no discernible effect of the eclipse on the wind-direction or MSLP time series, and therefore we can discount any localized eclipse cyclone effect over Britain during this event. Similar changes in air temperature and wind speed are observed for Iceland, where conditions were generally clearer, but here too there was no evidence of an eclipse cyclone; in the Faroes, there was a much more muted meteorological signature. This article is part of the themed issue ‘Atmospheric effects of solar eclipses stimulated by the 2015 UK eclipse’.

scholarly journals Evaluating the association between extreme heat and mortality in urban Southwestern Ontario using different temperature data sources

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kristin K. Clemens ◽  
Alexandra M. Ouédraogo ◽  
Lihua Li ◽  
James A. Voogt ◽  
Jason Gilliland ◽  
...  
Keyword(s):  
High Resolution ◽  
Local Level ◽  
Extreme Temperature ◽  
Temperature Data ◽  
Data Sources ◽  
Population Exposure ◽  
Weather Station ◽  
Station Data ◽  
Weather Station Data ◽  
Weather Stations

AbstractUrban areas have complex thermal distribution. We examined the association between extreme temperature and mortality in urban Ontario, using two temperature data sources: high-resolution and weather station data. We used distributed lag non-linear Poisson models to examine census division-specific temperature–mortality associations between May and September 2005–2012. We used random-effect multivariate meta-analysis to pool results, adjusted for air pollution and temporal trends, and presented risks at the 99th percentile compared to minimum mortality temperature. As additional analyses, we varied knots, examined associations using different temperature metrics (humidex and minimum temperature), and explored relationships using different referent values (most frequent temperature, 75th percentile of temperature distribution). Weather stations yielded lower temperatures across study months. U-shaped associations between temperature and mortality were observed using both high-resolution and weather station data. Temperature–mortality relationships were not statistically significant; however, weather stations yielded estimates with wider confidence intervals. Similar findings were noted in additional analyses. In urban environmental health studies, high-resolution temperature data is ideal where station observations do not fully capture population exposure or where the magnitude of exposure at a local level is important. If focused upon temperature–mortality associations using time series, either source produces similar temperature–mortality relationships.

scholarly journals Topoclimatic Zoning and Representative Areas as Determined by an Automatic Weather Station (AWS) Network in the Atacama Region, Chile

Atmosphere ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 611
Author(s):  
Donna Cortez ◽  
Rodrigo Padilla ◽  
Sebastián Herrera ◽  
Juan Uribe ◽  
Manuel Paneque
Keyword(s):  
Principal Component ◽  
Weather Station ◽  
Automatic Weather Station ◽  
Climate Information ◽  
Weather Information ◽  
Climate Knowledge ◽  
Weather Stations ◽  
And Cluster Analysis ◽  
Representative Area ◽  
Climatic Information

Climate information is crucial to the management and profitability of key development sectors involving agriculture, hydrologic resources, natural hazards, and energy. Climate knowledge, real-time weather information, and climate predictions reliability all contribute to the planning and management of socioeconomic activities and sustainable development. Automatic weather stations (AWSs) are remotely operated and facilitate the recording of meteorological information for unoccupied and out-of-reach areas. However, the representative area of the Atacama region is unknown, whose uniqueness is largely determined by the topography of the terrain. This paper describes the topoclimatic zoning of the Atacama region, based on the identification of homogeneous climatic and topographic areas, using climatic information, principal component analysis, and cluster analysis. Topoclimatic zoning was used to determine the representative area of the AWSs. Sixty-one regional topographic units were identified as equivalent to the representative area of the AWS. The directly represented area was estimated at 2365 km2 (3.13% of the regional total), the indirectly represented area was 8725 km2 (11.53%), and the unrepresented area was 64,561 km2 (85.34%). This large unrepresented area displays potential zones for future AWS installations, which can improve both the efficiency of the regional meteorological network and access to quality climate information.

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