Estimating daily mean temperature from synoptic climate observations

Ma, Yuting; Guttorp, Peter

doi:10.1002/joc.3510

Cited by 25 publications

(18 citation statements)

References 6 publications

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“…Scandinavian countries developed a special formula to estimate mean T a 31. For example, Sweden still uses the Ekholm–Modén formula from 1916, in which the mean T a is a linear combination of the T max , T min , and measurements of T a at 6, 12, and 18 h UTC31.…”

Section: Discussionmentioning

confidence: 99%

Sampling Biases in Datasets of Historical Mean Air Temperature over Land

Wang

2014

Sci Rep

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Global mean surface air temperature (Ta) has been reported to have risen by 0.74°C over the last 100 years. However, the definition of mean Ta is still a subject of debate. The most defensible definition might be the integral of the continuous temperature measurements over a day (Td0). However, for technological and historical reasons, mean Ta over land have been taken to be the average of the daily maximum and minimum temperature measurements (Td1). All existing principal global temperature analyses over land rely heavily on Td1. Here, I make a first quantitative assessment of the bias in the use of Td1 to estimate trends of mean Ta using hourly Ta observations at 5600 globally distributed weather stations from the 1970s to 2013. I find that the use of Td1 has a negligible impact on the global mean warming rate. However, the trend of Td1 has a substantial bias at regional and local scales, with a root mean square error of over 25% at 5° × 5° grids. Therefore, caution should be taken when using mean Ta datasets based on Td1 to examine high resolution details of warming trends.

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Section: Discussionmentioning

confidence: 99%

Sampling Biases in Datasets of Historical Mean Air Temperature over Land

Wang

2014

Sci Rep

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“…While daily maximum temperatures may be more suitable for identifying single warming events, digitized values of daily minima and maxima exist only for the latest 40-80 years. Despite different methodologies used to calculate daily mean temperature, both through time and for the different Nordic countries (Ma and Guttorp 2013;Nordli and Tveito 2008), we consider the daily mean temperature data series to be comparable for the statistical evaluation.…”

Section: Data and Study Area A Weather Station Datamentioning

confidence: 99%

Changes in Winter Warming Events in the Nordic Arctic Region

et al. 2016

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In recent years extreme winter warming events have been reported in arctic areas. These events are characterized as extraordinarily warm weather episodes, occasionally combined with intense rainfall, causing ecological disturbance and challenges for arctic societies and infrastructure. Ground-ice formation due to winter rain or melting prevents ungulates from grazing, leads to vegetation browning, and impacts soil temperatures. The authors analyze changes in frequency and intensity of winter warming events in the Nordic arctic region-northern Norway, Sweden, and Finland, including the arctic islands Svalbard and Jan Mayen. This study identifies events in the longest available records of daily temperature and precipitation, as well as in future climate scenarios, and performs analyses of long-term trends for climate indices aimed to capture these individual events. Results show high frequencies of warm weather events during the 1920s-30s and the past 15 years , causing weak positive trends over the past 90 years . In contrast, strong positive trends in occurrence and intensity for all climate indices are found for the past 50 years with, for example, increased rates for number of melt days of up to 9.2 days decade 21 for the arctic islands and 3-7 days decade 21 for the arctic mainland. Regional projections for the twenty-first century indicate a significant enhancement of the frequency and intensity of winter warming events. For northern Scandinavia, the simulations indicate a doubling in the number of warming events, compared to 1985-2014, while the projected frequencies for the arctic islands are up to 3 times higher.

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“…The Mean Method (MM) [24] is a simple average or arithmetic mean of the entire data collected over a long period of time. The values which exceed the threshold denote error whereas those below the threshold suggest normal working of the system.…”

Section: Mean Methodsmentioning

confidence: 99%

An approach to real-time fault detection in health monitoring of offshore wind-farms

Agarwal

Kishor

2014

2014 IEEE Asia Pacific Conference on Wireless and Mobile

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Wind is a clean resource for electricity generation. However, its power is difficult to harness due to the effect of environmental parameters which cause fault occurrences in the wind-turbine towers and decrease the efficiency of the system. We propose Flexible Threshold Selection and Fuzzy inference system based Fault Detection System (FTSFFDS), which allows autonomous and continuous monitoring of the wind farm in realtime and gives warnings in case of fault occurrences. This is a simple and efficient method which forms the basis for the structural health monitoring of Offshore wind-farms. The system uses WSN in offshore wind-farm to sense the parameters and transmit the readings to the remote observer. The method utilizes adaptive thresholds to cater to the readings at different times of the day. The decision for new threshold is made when the correlation between the datasets is low. The proposed method is efficient in reducing the overall size of the data packets transmitted by the sensor nodes and thus saving energy in the WSN. The simulations confirm that the energy efficiency of the system is increased by nearly ten times compared to standard protocols e.g. LEACH. This paper is an extension to our previous research-work which proposed an application-specific clustering and routing protocol NETCRP (Network Lifetime Enhancing Clustering and Routing Protocol).

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Estimating daily mean temperature from synoptic climate observations

Cited by 25 publications

References 6 publications

Sampling Biases in Datasets of Historical Mean Air Temperature over Land

Sampling Biases in Datasets of Historical Mean Air Temperature over Land

Changes in Winter Warming Events in the Nordic Arctic Region

An approach to real-time fault detection in health monitoring of offshore wind-farms

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