A Novel Global Freeze-Thaw State Detection Algorithm Based on Passive L-Band Microwave Remote Sensing

Knowing the Freeze-Thaw (FT) state of the land surface is essential for many aspects of weather forecasting, climate, hydrology, and agriculture. Near-surface air temperature and land surface temperature are usually used in meteorology to infer the FT-state. However, the uncertainty is large because both temperatures can hardly be distinguished from remote sensing. Microwave L-band emission contains rather direct information about the FT-state because of its impact on the soil dielectric constant, which determines microwave emissivity and the optical depth profile. However, current L band-based FT algorithms need reference values to distinguish between frozen and thawed soil, which are often not known sufficiently well. We present a new FT-state detection algorithm based on the daily variation of the H-polarized brightness temperature of the SMAP L3c FT global product for the northern hemisphere, which is available from 2015 to 2021. The exploitation of the daily variation signal allows for a more reliable state detection, particularly during the transitions periods, when the near-surface soil layer may freeze and thaw on sub-daily time scales. The new algorithm requires no reference values; its results agree with the SMAP FT state product by up to 98 % in summer and up to 75 % in winter. Compared to the FT state inferred indirectly from the 2-m air temperature of the ERA5-land reanalysis, the new FT algorithm has a similar performance as the SMAP FT product. The most significant differences occur over the midlatitudes, including the Tibetan plateau and its downstream area. Here, daytime surface heating may lead to daily FT transitions, which are not considered by the SMAP FT state product but are correctly identified by the new algorithm. The new FT algorithm suggests a 15 days earlier start of the frozen-soil period than the ERA5-land’s 2-m air temperature estimate. This study is expected to extend L-band microwave remote sensing data for improved FT detection.

Behavior and Habitat Use Remain Diverse and Variable in Modern Zoological Exhibits over the Long-Term: Case Studies in 5 Species of Ursidae

Long-term evaluations of whether modern zoological exhibits help to maintain variation in the behavior of zoo animals are lacking despite the hope that animals avoid falling into monotonous patterns of behavior or boredom. This study evaluated changes in behavior and habitat use over multi-year periods in nine individuals of five bear species at two zoological facilities. Behavioral data gathered over months to years were analyzed graphically for trends in the direction of change. The habitat use dynamics were assessed graphically by looking for trends in the entropy values over time. We found that the activity budgets remained diverse and were dynamic over time, more so in younger compared to older bears. Changes in behavior suggesting positive welfare were observed, while changes that may reflect declining welfare seemed more likely to be due to age or seasonality. The observed behavioral changes suggest that the bears did not become bored with their habitats; there was likely one to several hours of daily variation in behavior, and stereotypy was rare. The diversity in the habitat use decreased over time as the animals settled into patterns of use reflecting preferences for certain areas of their habitats.

Downscaling Winter Daily PM2.5 Concentrations From Large-Scale Meteorological Dataset -a Case Study for a City in North China

Winter air pollution in North China becomes a serious environmental problem in recent years, which has aroused a widespread concern. Estimating PM2.5 concentration is necessary for the government to take actions in leading times, or to reproduce the historical values. In this study, we attempt to construct statistical downscaling (SD) models based on large-scale meteorological variables, to estimate the PM2.5 in Jiaozuo, a city in the heavy-pollution area of North China. Predictors were screened from large-scale meteorological variables, by selecting the grid boxes with the values highly correlated to the PM2.5 in Jiaozuo. Correlation maps show that PM2.5 is usually related to comparatively low pressure and high relative humidity at the lower atmosphere and comparatively high pressure at the upper atmosphere, high air temperature in all the troposphere and weak winter monsoonal winds. After training the SD models with the winter samples during 2014-2017, the daily PM2.5 is simulated with correlation coefficients of 0.607 (mean) and 0.548 (maximum) to the observation, by the logarithmic transformed PM2.5 values. The SD models can roughly reflect the daily variation of PM2.5. Compared to the PM2.5 model based on the local meteorological data, larger correlations are obtained (0.57 versus 0.51-0.53, without logarithmic transformation). In the days of “APEC Blue Sky” and the “SCO Blue Sky” with emissions largely reduced by strict controls on industry and traffic in surrounding areas, low PM2.5 is indeed observed, however, the SD models without considering any change of emissions also produced low PM2.5 simulations, implying that the large-scale circulation plays a main role in the daily variation of air pollution. Similar effects were obtained for the traffic-control month and the Chinese Spring festival with massive fireworks burning. Basing on the large-scale reanalysis variables and assuming the same emission level as that in the model training period, the downscaled winter PM2.5 has a significant decreasing trend during 1979-2017.

Extended isolation forest – application to outlier detection in geomagnetic data

The aim of this study is to present a method for detection of outliers in the time series of total intensity of geomagnetic field using Extended Isolation Forest algorithm. The method is consisted of three steps: 1) generation of additional features that take into account the regular daily variation and smooth behaviour of normal data, 2) detection of potential outliers based on ensemble of extended isolating trees and 3) subsequent refinement based on difference between the outlier and its replacement with interpolated value. Application of the method for detection of outliers in yearly time series of the total geomagnetic field at Ak-Suu and Kegety stations showed that the algorithm identifies both global and contextual outliers. Average classification metrics for the method are characterized as high and have the following values: precision 94.3%, recall 93.9% and F-score 94.5%, and probabilities of errors of the first and second kind are comparable to similar algorithms used for detection of outliers in magnetograms of different sampling rate.

Do hotter temperatures increase the incidence of self-harm hospitalisations?

A relationship between air temperature and the incidence of suicide has been established in a number of previous studies. Interestingly, the relationship between geographical variation in temperature and suicide incidence has generally been found to be negative, while the relationship between temporal variation in temperature and suicide incidence has generally been found to be positive. It is less clear, however, how temperature relates to the incidence of self-harm. This topic is of particular importance given the presence of ongoing global warming. This study investigated the relationship between temperature and the incidence of self-harm resulting in hospitalisation in New Zealand. Self-harm hospitalisations by date and district for 1993–2009 were obtained from the Ministry of Health. Meteorological data was obtained from NIWA. Generalized linear mixed models were used to estimate the effects of three different components of variation in temperature: Geographical, seasonal, and irregular. Irregular (random) daily variation in temperature had a modest positive relationship with the incidence of acts of self-harm resulting in hospitalisation, with about 0.7% extra incidents for every 1°C increase in temperature. However, there was no strong evidence for a positive effect of either seasonal or geographical variation in temperature. We conclude that temperature does appear to bear some relation to the incidence of self-harm, with irregular daily variation in temperature having a positive effect. However, inconsistencies in the effects of different components of variation in temperature make it challenging to accurately predict how global warming will influence the incidence of self-harm.

Diurnal Ruminal pH and Temperature Patterns of Steers Fed Corn or Barley-Based Finishing Diets

This study evaluated the effects of corn or barley finishing diets on ruminal pH and temperature and their relationship to feed intake events using continuous reticulorumen monitoring of feedlot steers. Average daily ruminal pH and temperature were not impacted (p ≥ 0.17) by diet. However, diet did affect daily variation of ruminal pH and temperature (p < 0.01). Average hourly ruminal pH displayed a diet by hour post-feeding interaction (p < 0.01), where barley-fed steers had greater (p < 0.01) ruminal pH than corn-fed steers at 0, 1, 18, 19, 20, 21, 22 and 23 h post feeding, but had lower (p ≤ 0.05) ruminal pH than corn-fed steers at 6, 7, and 8 h post-feeding. Variation in ruminal pH hour post-feeding also displayed a diet by hour post-feeding interaction (p < 0.01), where barley-fed steers had greater (p ≤ 0.03) variation in ruminal pH at hours 1–17 post-feeding but did not differ (p ≥ 0.16) at 0, 18, 19, 20, 21, 22 and 23 h post-feeding. Additionally, average hourly ruminal temperature exhibited a diet by hour post-feeding interaction (p < 0.01). In summary, basal grain interacted with time post-feeding influencing ruminal pH and temperature in feedlot steers.