With digital equipment becoming increasingly networked, either on wired or wireless networks, for personal and professional use alike, distributed software systems have become a crucial element in information and communications technologies. The study of these systems forms the core of the ARLES' work, which is specifically concerned with defining new system software architectures, based on the use of emerging networking technologies. In this context, we concentrate on the study of distributed systems which bring to life the vision of ubiquitous computing systems, also known as ambient intelligence.

반도체 공정 overview

We present a new version of the Met Office Hadley Centre/Climatic Research Unit global surface temperature dataset, HadCRUT5. HadCRUT5 presents monthly average near-surface temperature anomalies, relative to the 1961-1990 period, on a regular 5° latitude by 5° longitude grid from 1850 to 2018. HadCRUT5 is a combination of sea-surface temperature measurements over the ocean from ships and buoys and near-surface air temperature measurements from weather stations over the land surface. These data have been sourced from updated compilations and the adjustments applied to mitigate the impact of changes in sea-surface temperature measurement methods have been revised. Two variants of HadCRUT5 have been produced for use in different applications. The first represents temperature anomaly data on a grid for locations where measurement data are available. The second, more spatially complete, variant uses a Gaussian process based statistical method to make better use of the available observations, extending temperature anomaly estimates into regions for which the underlying measurements are informative. Each is provided as a 200-member ensemble accompanied by additional uncertainty information. The combination of revised input datasets and statistical analysis results in greater warming of the global average over the course of the whole record. In recent years, increased warming results from an improved representation of Arctic warming and a better understanding of evolving biases sea-surface temperature measurements from ships. These updates result in greater consistency with other independent global surface temperature datasets, despite their different approaches to dataset construction, and further increase confidence in our understanding of changes seen.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2019JD032361

An Updated Assessment of Near-Surface Temperature Change From 1850: The HadCRUT5 Data Set

Modeling global solar radiation using Particle Swarm Optimization (PSO)

Recently, several methods have been introduced and applied to estimate daily air surface temperature (Ta) using MODIS land surface temperature data (MODIS LST). Among these methods, the most common used method is statistical modeling, and the most applied algorithms are linear/multiple linear regression models (LM). There are only a handful of studies using machine learning algorithm models such as random forest (RF) or cubist regression (CB). In particular, there is no study comparing different combinations of four MODIS LST datasets with or without auxiliary data using different algorithms such as multiple linear regression, random forest, and cubist regression for daily Ta-max, Ta-min, and Ta-mean estimation. Our study examines the mentioned combinations of four MODIS-LST datasets and shows that different combinations and differently applied algorithms produce various Ta estimation accuracies. Additional analysis of daily data from three climate stations in the mountain area of North West of Vietnam for the period of five years (2009 to 2013) with four MODIS LST datasets (AQUA daytime, AQUA nighttime, TERRA daytime, and TERRA nighttime) and two additional auxiliary datasets (elevation and Julian day) shows that CB and LM should be applied if MODIS LST data is used solely. If MODIS LST is used together with auxiliary data, especially in mountainous areas, CB or RF is highly recommended. This study proved that the very high accuracy of Ta estimation (R2 > 0.93/0.80/0.89 and RMSE ~1.5/2.0/1.6 °C of Ta-max, Ta-min, and Ta-mean, respectively) could be achieved with a simple combination of four LST data, elevation, and Julian day data using a suitable algorithm.

/pdf/comparison-of-multiple-linear-regression-cubist-regression-2hep5z2ce1.pdf

Comparison of Multiple Linear Regression, Cubist Regression, and Random Forest Algorithms to Estimate Daily Air Surface Temperature from Dynamic Combinations of MODIS LST Data

Urban air temperature is considered a significant variable for a variety of urban issues, and analyzing the spatial patterns of air temperature is important for urban planning and management. However, insufficient weather stations limit accurate spatial representation of temperature within a heterogeneous city. This study used a random forest machine learning approach to estimate daily maximum and minimum air temperatures (Tmax and Tmin) for two megacities with different climate characteristics: Los Angeles, USA, and Seoul, South Korea. This study used eight time-series land surface temperature (LST) data from Moderate Resolution Imaging Spectroradiometer (MODIS), with seven auxiliary variables: elevation, solar radiation, normalized difference vegetation index, latitude, longitude, aspect, and the percentage of impervious area. We found different relationships between the eight time-series LSTs with Tmax/Tmin for the two cities, and designed eight schemes with different input LST variables. The schemes were evaluated using the coefficient of determination (R2) and Root Mean Square Error (RMSE) from 10-fold cross-validation. The best schemes produced R2 of 0.850 and 0.777 and RMSE of 1.7 °C and 1.2 °C for Tmax and Tmin in Los Angeles, and R2 of 0.728 and 0.767 and RMSE of 1.1 °C and 1.2 °C for Tmax and Tmin in Seoul, respectively. LSTs obtained the day before were crucial for estimating daily urban air temperature. Estimated air temperature patterns showed that Tmax was highly dependent on the geographic factors (e.g., sea breeze, mountains) of the two cities, while Tmin showed marginally distinct temperature differences between built-up and vegetated areas in the two cities.

Estimation of daily maximum and minimum air temperatures in urban landscapes using MODIS time series satellite data

The relationship between satellite land surface temperature (LST) and ground-based observations of 2m air temperature (T2m) is characterised in space and time using >17 years of data. The analysis uses a new monthly LST climate data record (CDR) based on the Along-Track Scanning Radiometer (ATSR) series, which has been produced within the European Space Agency GlobTemperature project (http://www.globtemperature.info/). Global LST-T2m differences are analysed with respect to location, land cover, vegetation fraction and elevation, all of which are found to be important influencing factors. LSTnight (~10 pm local solar time, clear-sky only) is found to be closely coupled with minimum T2m (Tmin, all-sky) and the two temperatures generally consistent to within ±5 °C (global median LSTnight- Tmin= 1.8 °C, interquartile range = 3.8 °C). The LSTday (~10 am local solar time, clear-sky only)-maximum T2m (Tmax, all-sky) variability is higher (global median LSTday- Tmax= -0.1°C, interquartile range = 8.1 °C) because LST is strongly influenced by insolation and surface regime. Correlations for both temperature pairs are typically >0.9 outside of the tropics. The monthly global and regional anomaly time series of LST and T2m – which are completely independent data sets - compare remarkably well. The correlation between the data sets is 0.9 for the globe with 90% of the CDR anomalies falling within the T2m 95% confidence limits. The results presented in this study present a justification for increasing use of satellite LST data in climate and weather science, both as an independent variable, and to augment T2m data acquired at meteorological stations.

A spatiotemporal analysis of the relationship between near-surface air temperature and satellite land surface temperatures using 17 years of data from the ATSR series

This paper presents an analysis of the relationship between land surface temperatures (LST) and screen-level air temperatures (T2m) using in situ observations from 19 Atmospheric Radiation Measurement (ARM) deployments located in a range of geographical regimes. The diurnal cycle is resolved using one-minute observations: a particular focus of the study is on the relationship between daily extremes of LST (LSTmax, LSTmin) and T2m (Tmax, Tmin). Temperature differences are analysed with respect to cloud, wind speed and snow cover. Under cloud-free, low wind speed conditions, day time LST is often several degrees Celsius (°C) higher than T2m at low-to-mid latitudes, and at high latitudes during the summer months. In contrast, LST and T2m are often close (e.g. within 2 °C) under cloudy and/or moderate-to-high wind speed conditions, or when solar insolation is low or absent. LSTmin and Tmin are generally well correlated (r >0.8, often r >0.9), while seasonal correlations between LSTmax and Tmax are weaker (r >0.6, often r >0.8). At high latitudes, LST and T2m are well coupled in Spring/Autumn/Winter; the relationship between LST and T2m tends to weaken with decreasing latitude. The timing of daily extremes is also investigated and it is found that LSTmin and Tmin typically occur close to sunrise, with Tmin occurring slightly after LSTmin. LSTmax occurs close to solar noon, with Tmax typically occurring 1-3 hours later. This study will inform temperature data users on differences between LST and T2m, and aid development of methods to estimate T2m using satellite LSTs.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2016JD025318

An in situ-based analysis of the relationship between land surface "skin" and screen-level air temperatures

Observations of daily minimum and maximum land air temperatures, Tmin and Tmax, have traditionally been obtained through in situ observations at meteorological stations. While the station network is extensive, many land masses are poorly observed. Moreover, observations at stations are “point” observations and may not be representative of air temperatures at neighboring locations. Satellites provide the means to observe surface skin temperatures at spatial scales of tens of meters to kilometers. But although skin and near-surface air temperatures may be strongly coupled, the two quantities can differ by several degrees over land, where the magnitude of the difference is variable in both space and time. This study describes a method for estimating daily Tmin and Tmax at the pixel scale using geostationary satellite data, providing spatially detailed observations for areas unobserved in situ. A dynamic multiple linear regression model is developed using daily minimum and maximum land surface temperature (LSTmin and LSTmax), fraction of vegetation, distance from coast, latitude, urban fraction, and elevation as predictors. The method is demonstrated over Europe for 2012–2013; evaluation with collocated station observations indicates a mean satellite-minus-station bias of 0.0 to 0.5°C with root-mean-square difference of 2.3 to 2.7°C. The data derived here are not designed to replace traditional gridded station air temperature data sets, but to augment them. Satellite surface temperature data usually have larger uncertainties than in situ data sets, but they can offer spatial detail and coverage that the latter may not provide.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2014JD022438

Daily minimum and maximum surface air temperatures from geostationary satellite data

https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/asl.721

Predictability of European winter 2015/2016

The Met Office produces sunshine maps for the UK based on observations from ground-based instruments. These maps have uncertainty owing to the number and distribution of stations that record sunshine. This article describes a method for estimating sunshine duration from geostationary satellite data that provides near-complete spatial coverage. Comparisons with daily ground-based observations from Kipp & Zonen sensors over four months indicate a mean satellite minus station bias and standard deviation of -0.2 and 1.6 hours, respectively. Satellite-station correlation is 0.89. The new data set could be used to improve the interpolation of station observations in the Met Office maps.

/pdf/estimating-daily-sunshine-duration-over-the-uk-from-31s2cxl0gh.pdf

Elizabeth Good

Papers

A spatiotemporal analysis of the relationship between near-surface air temperature and satellite land surface temperatures using 17 years of data from the ATSR series

An in situ-based analysis of the relationship between land surface "skin" and screen-level air temperatures

Daily minimum and maximum surface air temperatures from geostationary satellite data

Predictability of European winter 2015/2016

Estimating daily sunshine duration over the UK from geostationary satellite data