Annual Statistics Research Articles

Seasonal Characteristics of Agricultural Product Circulation Network: A Case Study in Beijing, China

Agricultural product circulation is an appropriate way to optimize the distribution of agricultural resources and maintain food safety. The seasonality of agriculture leads to seasonal variations in agricultural product circulation. Previous studies constructed origin–destination networks based on annual statistics to investigate the static structure of agricultural product circulation networks from a single view, failing to capture the seasonal and multi-dimensional characteristics in agricultural product circulation. This study presents a multi-view analytical framework used to investigate the seasonal characteristics of an agricultural product circulation network. First, agricultural product circulation networks in different seasons were constructed with mass freight trajectory data through trajectory mining technology. Then, the seasonal characteristics of agricultural product circulation were, respectively, analyzed from a macro-view (networks), meso-view (edges) and micro-view (nodes). A case study was conducted in Beijing, China. It is argued that: (1) The presented method for extracting agricultural trip chains based on massive freight trajectories is feasible for the construction of agricultural product circulation networks. (2) The agricultural product circulation networks in four seasons exhibit an obvious hierarchical and radial structure. South China has a higher network density in winter and spring, whereas northeast and northwest China are the opposite. (3) A total of 80% of the linkage strength is concentrated, on average, in 35.3% of city-pairs in four seasons, where the agglomeration effect and hub status of the linking cities is more prominent in summer and autumn. (4) A total of 316 cities form Beijing agricultural product circulation networks, 48.1% of which are mainly served by Beijing agricultural product circulation in winter and spring, which is 2.7 times more than cities served in summer and autumn. These findings extend the scientific understanding of the agricultural product supply chain from a dynamic and multi-dimensional view, which provides essential information for optimizing sustainable agri-food systems and ensuring food security.

Spatiotemporal Evolution and Influencing Factors of Population Growth Transition in China during the COVID-19 Pandemic

At present, China has lost its demographic dividend. Meanwhile, in the face of the twin challenges of rising living costs and the uneven distribution of educational resources, the fertility intentions of ordinary people are rapidly declining. In the background, China’s latest birth incentive policy has not had the desired effect. Especially with the COVID-19 pandemic, these issues have become more complicated, making it more difficult to achieve policy goals. Analysis of changing characteristics and influencing mechanisms in China’s current stage of population growth is therefore significant. Such analysis can help improve China’s population structure and preserve the advantage of human resource endowment. In this paper, we use data from 2008, 2012, 2016, and 2020 as made available from the resources of China’s National Bureau of Statistics and National Health Commission. These included annual macro statistics, seventh census data, and COVID-19 pandemic data, allowing us to analyze the influencing mechanism of China’s population growth by using the Geographic Detector Model. The research revealed the dependency ratio as the primary factor influencing spatial differentiation of population growth in China, indicating that the dependency burden plays a role in inhibiting population growth. The secondary factor showed different changes in stages. At the same time, any two factors showed more substantial explanatory power after the interaction, meaning the spatial distribution of China’s population growth results from the joint influence of many factors. The strong interaction was mainly concentrated around the dependency ratio and were with women’s political participation and internet coverage. However, under the impact of the COVID-19 pandemic, the explanatory power of traditional factors was diluted, leading to a decline in the strength of interaction.

Nanomaterials pollutant's emission influencing outdoor recreation in China: Analytical strategies proceeding to sustainable environment

The global demand for bioenergy and nanomaterial utilization has significantly contributed to their source depletion. The impact of outdoor pollutant emissions on residents' health, behavior, physical activity, and sleeping behavior is much less explored. Producing biofuels from organic waste has the potential to unite budget-friendliness and long-term sustainability. Primary biofuels are produced specifically to mitigate environmental problems, and secondary biofuels are produced specifically to mitigate other environmental problems. The high level of pollutants ina certain area can severely restrict human activities and ultimately influence their whole life. The study aims to empirically evaluate the influence of nanomaterials pollutants emissions influencing outdoor recreation in China because its economy is based on fast-paced industrialization and urbanization. Various datasets were collected, including domestic and international tourists from the annual statistics for 2011–2020 from the National Bureau of Statistics of China. Ten years of annual METAR data of visibility (VR), temperature (T), relative humidity (Demirhan), and wind speed (WS) from 30 metrological stations at international airports. Annual average AOD (Aerosol's Optical Depth), O3 (Ozone), CO (Carbon Monoxide), SO4 (Sulphate), and PM2.5 (Particulate Matter) satellite imageries from 2011 to 2020 were obtained from NASA's official portal. The overall picture of satellite observations of pollutants over China describes that the highest level of AOD was observed in regions with greater population concentration than in other areas. We computed a statistical summary, correlation matrix, and Autoregressive Distributed Lag (ARDL) approach. Certain analyses were performed, like Unrestricted Error Correction Models (UECM) and Restricted Error Correction Model (RECM)), bound tests for cointegration, and multiplier estimations for short-term and long-term impacts. UECM and RECM models produced R2 > 0.90 with good F statistics and a p-value of <0.05. A bounds F-test for no cointegration on the ARDL model was performed, and the results show that the p-value = 1e-06 and F = 8.516 with alternative hypothesis = Possible cointegration. Bounds t-test for no cointegration was also performed on UECM model and the results show that t = −2.9066, Lower-bound I(0) = −3.43, Upper-bound I(1) = −5.37, p-value = 0.6138 and alternative hypothesis = Possible cointegration. The study's findings may help Chinese policymakers and stakeholders design effective information strategies to combat air pollution. Effective measures can help alleviate this devastating air quality factor, thus benefiting the tourism industry and enhancing the local inhabitants' welfare.

Preliminary Investigation of the Potentialities of a Mesoscale Meteorological Model to Reproduce Experimental Statistics of Rain Attenuation on Earth-Space Links

Current spatial resolutions achieved by mesoscale weather forecast models allow them to be used to generate the state of the lowest layers of the atmosphere over areas as small as a few square kilometers which corresponds to the typical size of the tropospheric area crossed by Earth-space links. Furthermore, they allow the evolution of the troposphere to be predicted with a time stamp of five minutes instead of every hour with large-scale weather forecast models which makes them attractive for radio propagation predictions for satellite communication applications. This paper aims at studying the capability of the Weather Research and Forecast (WRF) model coupled with an electromagnetic physical model to reproduce rain attenuation statistics for Earth-space paths at Ka-band. To this purpose, one year of propagation measurements collected at 20 GHz in different places at midlatitudes in Toulouse and Salon de Provence (France), Spino d’Adda (Italy), Aveiro (Portugal), and Madrid (Spain), at high latitudes in Svalbard (Norway) and at low latitudes in Kourou are used to make comparisons between simulations and measurements. Comparisons between the simulated and the experimental annual statistics considered in this paper provide encouraging results, with a similar accuracy as Recommendation ITU-R P.618–13 for midlatitude European locations and with better accuracy for a high latitude area in Svalbard and for an equatorial location in French Guiana.

An Observation Density Based Method for Independent Baseline Searching in GNSS Network Solution

With applications such as precise geodetic product generation and reference frame maintenance, the global GNSS network solution is a fundamental problem that has constantly been a focus of concern. Independent baseline search is a prerequisite step of the double-differenced (DD) GNSS network. In this process, only empirical methods are usually used, i.e., the observation-max (OBS-MAX), which allows for obtaining more redundant DD observations, and the shortest-path (SHORTEST), which helps to better eliminate tropospheric and ionospheric errors between stations. Given the possible limitations that neither of the methods can always guarantee baselines of the highest accuracy to be selected, a strategy based on the ‘density’ of common satellites (OBS-DEN) is proposed. It takes the number of co-viewing satellites per unit distance between stations as the criterion. This method ensures that the independent baseline network has both sufficient observations and short baselines. With single-day solutions and annual statistics computed with parallel processing, the method demonstrates that it has the ability to obtain comparable or even higher positioning accuracy than the conventional methods. With a clearer meaning, OBS-DEN can be an option alongside the previous methods in the independent baseline search.

Forcasting Natural Gas Consumption in Nigeria using the Modified Grey Model (MGM(1,1,⊗b))

Accurate prediction of the natural gas consumption in Nigeria is crucial to Gas management. This study utilizes the improved Grey model (MGM(1,1,⊗b)), which is an improvement of the modified Grey model (MGM(1,1)), to forecast the natural gas consumption of Nigeria for the year 2021 to 2025. A secondary data retrieved from the NNPC 2019 annual statistics bulletin was used to build a model for this prediction. Noting that MGM(1,1) model uses the Grey action quantity as a unique real number which do not reflect the uncertainty nature of Grey systems. A model (MGM(1,1,⊗b)) was developed such that it extends the MGM(1,1) model to retain the uncertainty nature of Grey systems. The new modified Grey model (MGM(1,1,⊗b)) was used to make prediction of the natural gas consumption of Nigeria and the results shows that the (MGM(1,1,⊗b)) model gives a prediction interval which the actual value is bracketed. This implies that natural gas consumption of Nigeria for 2021 to 2025 lies within the (MGM(1,1,⊗b)) model prediction values for the same year.

We Can Reduce Breast Cancer Mortality

In my search for a topic for this column, I turned to my tried-and-true strategy of choosing a condition or issue that is featured in the month when the article will be published. I can usually find one that primarily affects women and something that I haven’t written about recently (or ever). As I did my research for this article, I selected a serious subject that touches on several content areas in my writing formula. October is dedicated to raising awareness about breast cancer, a disease that affects people across the adult life span. In their annual statistics published in January 2022, 1 Siegel R.L. Miller K.D. Fuchs H.E. Jamal A. Cancer statistics, 2022. CA Cancer J Clin. 2022; 72: 7-33 Crossref PubMed Scopus (1334) Google Scholar the American Cancer Society reported an overall 32% decline in death rates from all cancer in the United States between 1991 and 2019. Cancer remains the second most common cause of death; cardiovascular disease is still number one. Some of the decrease in cancer deaths is attributed to a decline in lung cancer due to smoking cessation and the increased survival rates for lung cancer because of earlier detection. Unfortunately, breast cancer rates in aggregate have not had the same downward progression, remaining static over the last decade. However, for Black women, breast cancer mortality has increased and has become the number one cause of death. 1 Siegel R.L. Miller K.D. Fuchs H.E. Jamal A. Cancer statistics, 2022. CA Cancer J Clin. 2022; 72: 7-33 Crossref PubMed Scopus (1334) Google Scholar Although Black women have a lower incidence of breast cancer, their risk of dying from the disease is more than 40% higher than that for White women. 1 Siegel R.L. Miller K.D. Fuchs H.E. Jamal A. Cancer statistics, 2022. CA Cancer J Clin. 2022; 72: 7-33 Crossref PubMed Scopus (1334) Google Scholar There are a number of institutional and social structural factors contributing to the disparities in morbidity and mortality for breast cancer and other diseases in Black women. These have existed for generations, but only recently have they been examined and acknowledged. Section Editor Denise G. Link, PhD, WHNP-BC, FAAN, FAANP, is a clinical professor emerita at Arizona State University Edson College of Nursing and Health Innovation in Phoenix. She can be reached at [email protected] .

Cement Industry and Climate Change in Nigeria: A Case Study of Dangote Obajana Cement Plant

Climate change has emerged as a global issue and one of the world's most pressing environmental challenges. Despite the contribution of the cement industry to the releases of carbon dioxide (the most significant contributor to GHG emissions) in Nigeria, only a few research writing focused on its influence on climate change. This paper, therefore, studied the variation in concentration of carbon dioxide (CO₂) within the environment of the Dangote Obajana Cement Plant. The CO₂ data used for the study were collected from the European Space Agency website. The satellite image data were downloaded from Google Earth and digitized to extract the study area using the WGS1984 geographic coordinate system while ArcMap packages were used for the data processing. The annual descriptive statistics for CO₂ variation for the years 2003, 2008, and 2018, showed that the minimum mean concentration was 373.451.07, 382.92054, and 0.950.17 while the maximum mean concentration was 374.931.0, 384.780.69, and 0.970.17. Bartlett's test for equal variances, with chi-squares statistics of 103.44 and 12.33, and a probability (Prob>chi2) value of 0.001 established that the minimum and maximum CO₂ emissions variances within the years are significantly different. The Bonferroni test revealed that all the years had CO₂ emissions that were significantly different from one another as their p-value was less than 0.05 with the minimum emission occurring in 2018 and the maximum in 2008. The study concluded that the influence of the Dangote Obajana Cement Plant's operation on the release of CO₂ concentrations across the study areas was insignificant since there was no uniformity in their concentrations.

Research and application of mobile workbench for clinical medical engineers in hospital

To explore the practical application value of mobile workbench for clinical medical engineers in hospital. Methods The clinical medical engineer team of a large hospital in Xi'an developed mobile workbench for clinical medical engineers based on Android system. This paper studies the management effect of medical equipment after using the mobile workbench. Results After adopting the mobile workbench for clinical medical engineers, the maintenance records integrity rate and the timeliness of maintenance response were significantly improved (c2=9.955, c2=22.650; P&lt;0.01), and there was no significant difference in the inspection pass rate after maintenance (c2=1.846; P&gt;0.05). At the same time, in the annual management benefit statistics, the mandatory measurement pass rate of the medical equipment, the perfectness rate of large equipment and the operating rate of large equipment were all improved, reaching 99.86%, 98.92%, 99.51%, respectively. The ratio of maintenance cost to equipment original value was 1.44%, which dropped significantly. Conclusions The mobile workbench for clinical engineers can significantly improve the maintenance and management effect of medical equipment and improve the annual management benefit of medical equipment. In the process of hospital digitalization and wireless construction, research on the construction of mobile work platform for clinical engineers will improve the overall image of the hospital and help realize the wireless digital hospitals.

Reconstruction of annual mean wind speed statistics at 100 m height of FINO1 and FINO2 masts with reanalyses and the geostrophic wind

Reconstruction of annual mean wind speed statistics at 100 m height of FINO1 and FINO2 masts with reanalyses and the geostrophic wind

Asymmetric Effects of Economic Development, Agroforestry Development, Energy Consumption, and Population Size on CO2 Emissions in China

The COVID-19 epidemic and the Russian–Ukrainian conflict have led to a global food and energy crisis, making the world aware of the importance of agroforestry development for a country. Modern agriculture mechanization leads to massive energy consumption and increased CO2 emissions. At the same time, China is facing serious demographic problems and a lack of consumption in the domestic market. The Chinese government is faced with the dilemma of balancing environmental protection with economic development in the context of the “double carbon” strategy. This article uses annual World Bank statistics from 1990 to 2020 to study the asymmetric relationships between agroforestry development, energy consumption, population size, and economic development on CO2 emissions in China using the partial least squares path model (PLS-PM), the autoregressive VAR vector time series model, and the Granger causality test. The results are as follows: (1) The relationship between economic development and carbon dioxide emissions, agroforestry development and carbon dioxide emissions, energy consumption and carbon dioxide emissions, and population size and carbon dioxide emissions are both direct and indirect, with an overall significant positive effect. There is a direct negative relationship between population size and carbon dioxide emissions. (2) The results of the Granger causality test show that economic development, energy consumption, and CO2 emissions are the causes of the development of agroforestry; economic development, agroforestry development, population size, and CO2 emissions are the causes of energy consumption; energy consumption is the cause of economic development and CO2 emissions; and agroforestry development is the cause of population size and energy consumption. (3) In the next three years, China’s agroforestry development will be influenced by the impulse response of economic development, energy consumption, and CO2 emission factors, showing a decreasing development trend. China’s energy consumption will be influenced by the impulse response of economic development, agroforestry development, population size, and CO2 emission factors, showing a decreasing development trend, followed by an increasing development trend. China’s CO2 emission will be influenced by the impulse response of energy consumption and agroforestry development. China’s CO2 emissions will be influenced by the impulse response of energy consumption and agroforestry development factors, showing a downward and then an upward development trend.

Correlation between biomass burning and air pollution in China: Spatial heterogeneity and corresponding factors

Biomass burning (i.e., forest fires or open-field burning) is strongly correlated with regional air pollution. However, the correlation varied in different regions because of the complex interaction with other driving factors, leaving a substantial gap in understanding the mechanism of air pollution formation. This study aimed to clarify the spatial heterogeneity and corresponding factors of the biomass burning-air pollution correlation in China in recent decades (2001–2019). Multisource datasets, including satellite observations, in situ measurements, and annual statistics, were adopted for the evaluation through mathematical statistics and spatial-temporal analysis. The results showed that biomass burning mainly occurred in Northeast, Central and South China, which decreased slightly by −153.08 km 2 ·yr −1 during the study period. Under this background, air quality improved significantly, particularly after 2013. The strongest correlations occurred in Northeast China at the spatial scale or in spring at the temporal scale. Notably, the correlation was affected by several factors and their complex interactions. Specifically, the dominant factors of the correlations were gross domestic product (GDP) in Northeast China, atmospheric humidity in Central China, and population in South China. In addition, these factors exerted significant interactions, which were characterized by the two-factor enhancement of temperature-GDP in Northeast China, the nonlinear enhancement of temperature-DEM in Central China, and the nonlinear enhancement of temperature-population in South China. The results of this study were helpful for capturing the physical correlation between biomass burning and air pollution, which would further support relevant air pollution management and control. • Biomass burning impact on air pollution were evaluated in pixel-wise. • Strong correlations located in Northeast, Central and South China. • Dominant factors were GDP, humidity and population in three hotspots regions. • Interactions had a stronger effect on correlation than single factors.

Forecasting Pistachio Production in Turkey: A Comparison of ARIMA, Grey, and Exponential Smoothing Models

This research aims to forecast pistachio production in Turkey using the Auto-Regressive Integrated Moving Average (ARIMA), the grey model (G.M. (1,1)), and Exponential Smoothing (Holt's and Brown's) models. The Food and Agriculture Organization provided the annual statistics on pistachio production in Turkey from 1961 to 2019, which was used in this study. Pistachio production is expected to increase between 2020 and 2030. The mean absolute error (MAE), root mean square error (RMSE), and the mean absolute percentage error (MAPE) statistics were used to assess the performance of the models. The G.M. (1,1) model was found to be significantly better than other models, and it was used to make future predictions. Results showed that the forecasted production value for 2020 was 157.80 thousand tons, rising to 302.86 thousand tons in 2030. Pistachio production is expected to increase during the next ten years.

126-LB: Type 2 Diabetes Mellitus (T2DM) Has Greater Adverse Effect on Mortality in Females than Males—Results of 10-Year Cohort Study

Females are more prone to obesity than males. This may lead to different outcomes. We studied if sex differences were seen in mortality outcomes in a longitudinal T2D cohort study. Data from a regional UK database (Salford) was taken between 2010-2020. Standardised Mortality Ratio (SMR) was established from actual versus expected deaths calculated from national annual statistics for mortality rate by sex and age. The SMR was then calculated for by each sex, year, age group, HbA1c level, and diabetes medication as taken by a patient in previous years. We included 9,558 patients with 88,102 patient years and 2,909 recorded deaths against 1,851 expected, SMR men 1.46 and women 1.72 (18% higher than men) . SMR increased slightly over time (Figure 1a) . SMR in younger females (&amp;lt;75 years) was higher than older females (Figure 1b) . SMR in females with high HbA1c (&amp;gt;86mmol/mol) was higher (Figure 1c) than lower HbA1c. Patients currently on insulin and GLP1 had much higher SMR than oral alone (Figure 1d) .SMR analysis of the oral drug use had SGLT2i 1.28; metformin 1.50; thiazolinedione 1.61; DPP4i 1.74; sulphonylurea 1.78. Note that average age and HbA1c varied across therapies. Diabetes has greater impact on women’s mortality than men for both age, and HbA1c. Indication bias will impact drug choice but lower SMR with SGLT2i and higher with sulphonylureas concords with trial data. Despite newer therapies, SMR for diabetes has not improved over time. Disclosure A. H. Heald: None. M. Stedman: None. I. Laing: None. A. Robinson: None. G. Rayman: None. M. B. Whyte: Consultant; AstraZeneca, Research Support; AstraZeneca, Sanofi.

Deaths associated with MDMA in the period 2000-2019.

The use of MDMA (3,4-methylenedioxymethamphetamine), also known as ecstasy, has increased in Norway in recent years. Since MDMA has the potential to be toxic and cause death, we studied whether increased availability and use correlates with the increase in MDMA-associated deaths. The study includes post-mortems with findings of MDMA in blood, linked to information about cause of death from the Norwegian Cause of Death Registry. These data were compared with the number of arrested drug drivers with MDMA detected in their blood as well as annual seizure statistics from Kripos (The National Criminal Investigation Service) in the period 2000-2019. In the period 2000-2019, MDMA was detected in 142 fatalities, and the cause of death was known for 132 of these. The number of annual MDMA-associated deaths varied from 1 to 18. The median MDMA concentration among the fatalities increased from 1.9 µmol/L (interquartile range (IQR) 0.9 to 5.0) in 2000-2004 to 3.8 µmol/L (1.4 to 12.0) in 2015-2019. In 47/132 (36%) of cases, MDMA and other central nervous system (CNS) stimulant drugs contributed to the death. Among arrested drug drivers with detected MDMA, the annual number of detected cases was 7-262 in this period, but the median concentration remained stable. MDMA may have contributed to numerous deaths in Norway. Increased availability, increased use and increased strength of contents seem to be significant.

Provisional Mortality Data - United States, 2021.

The CDC National Center for Health Statistics' (NCHS) National Vital Statistics System (NVSS) collects and reports annual mortality statistics using U.S. death certificate data. Because of the time needed to investigate certain causes of death and to process and review death data, final annual mortality data for a given year are typically released 11 months after the end of the calendar year. Provisional data, which are based on death certificate data received but not fully reviewed by NCHS, provide an early estimate of deaths before the release of final data. NVSS routinely releases provisional mortality data for all causes of death and for deaths involving COVID-19.* This report presents an overview of provisional U.S. mortality data for 2021, including a comparison of death rates for 2020 and 2021. In 2021, approximately 3,458,697 deaths† occurred in the United States. From 2020 to 2021, the age-adjusted death rate (AADR) increased by 0.7%, from 835.4 to 841.6 per 100,000 standard population. COVID-19 was reported as the underlying cause or a contributing cause in an estimated 460,513 (13.3%) of those deaths (111.4 deaths per 100,000). The highest overall death rates by age occurred among persons aged ≥85 years, and the highest overall AADRs by sex and race and ethnicity occurred among males and non-Hispanic American Indian or Alaska Native (AI/AN) and non-Hispanic Black or African American (Black) populations. COVID-19 death rates were highest among persons aged ≥85 years, non-Hispanic Native Hawaiian or other Pacific Islander (NH/OPI) and AI/AN populations, and males. For a second year, the top three leading causes of death by underlying cause were heart disease, cancer, and COVID-19. Provisional death estimates provide an early indication of shifts in mortality trends and can guide public health policies and interventions aimed at reducing mortality directly or indirectly associated with the pandemic and among persons most affected, including persons who are older, male, or from certain race and ethnic minority groups.

Incidence and Mortality of Cardiovascular Disease in the Republic of Kazakhstan: 2004-2017.

Background:We aimed to evaluate the impact of national health programs implemented in Kazakhstan from 2011 on CVD incidence and mortality.Methods:Incidence and mortality rates from CVD were studied in Kazakhstan from 2004 to 2017. The official data obtained from “Medinform” company were analyzed based on the annual population statistics.Results:There was an increase in the incidence of cardiovascular disease among the population of Kazakhstan from 1845.4 per 100,000 in 2004 to 2597.5 per 100,000 in 2017. This might be attributed to the implementation of the national health programs, which improved early CVD identification. Incidence of ischemic heart disease (IHD) was grown almost in all provinces of Kazakhstan during the study period. The mortality from cardiovascular disease had a considerable decline over the study period, in particular after 2010, it might be influenced by early diagnosis and provision of timely treatment.Conclusion:The experience of Kazakhstan national health programs shows improved identification of CVD and IHD and timely treatment for cardiovascular disease. A significant variation in incidence and mortality rates of cardiovascular disease was observed between the country provinces.

Automating rainfall recording: Ensuring homogeneity when instruments change

Sub-daily rainfall is used in modelling of small catchments, design of urban drainage, calculating soil erosion, understanding the water balance of vegetated catchments, and assessing the impact of climate change on short duration storms. Hence it is critical to ensure that sub-daily rainfall records are homogeneous, but with a global shift to automated measurement of rainfall, there has been a change in the rainfall recording instrumentation. Although changes in instrumentation should be documented, in many cases this is not the case and station metadata are missing. As result, studies of rainfall patterns, particularly those investigating trends, often cannot report their treatment of possible inhomogeneities and must assume the data is suitable for further analysis.Here, we argue that standard quality assurance methods for checking inhomogeneities in rainfall may not identify a change in the rainfall record due to a change in instrumentation. Through testing (1) an aggregation of rainfall to the coarsest instrument resolution, (2) removal of rainfall below a minimum rainfall depth, (3) removal of rainfall below a minimum accumulation (event) depth, and (4) a combination of the above adjustments, we present recommendations for ensuring sub-daily rainfall is homogenous with minimum alteration to the rainfall record.We assess the proposed methods using the case study of Australia’s sub-daily rainfall monitoring network, where, in 1996 the pluviograph network was switched to tipping buckets, creating a large-sample test bed with a known systematic instrumentation change. Our results show that event-based statistics are considerably more sensitive to instrument change than annually aggregated rainfall statistics and hence standard methods for quality assurance may not identify possible inhomogeneities. We suggest an aggregation of the data to the coarsest instrument resolution, with the removal of small rainfall accumulations, can alleviate inhomogeneities for event-based statistics, whereas, for annual statistics, aggregation to the coarsest instrument resolution is sufficient. In the absence of a known instrumentation change, removing rainfall at or below the instrument resolution is also a viable technique for improving rainfall record homogeneity for event-based statistics. These findings will aid future sub-daily rainfall studies by justifying the use of a minimum rainfall or event depth for subsequent analysis.

Comparison between Puff and Lagrangian Particle Dispersion Models at a Complex and Coastal Site

A comparison between a puff atmospheric dispersion model (hereafter: PuM) and a Lagrangian particle model (hereafter: LPM) was conducted for a real case of emissions from an industrial plant, in the context of a complex and coastal site. The PuM’s approach is well-known and widely adopted worldwide, thanks to the authoritative suggestions by the US-EPA for regulatory use as, according to the definitions included in its guidelines, an “alternative” to “preferred” models; LPMs are more advanced models and have gained reliability over the last two decades. Therefore, it is of interest to provide insights into the decision to adopt or recommend, in the field of atmospheric impact assessment, a more advanced, but more knowledge- and resource-intensive, modeling tool, rather than an established albeit less accurate one. An inter-comparison of the two approaches is proposed based on the use of various statistical and comparative parameters with the goal of studying their differences in reproducing maps of ground-level ambient concentration statistics for assessment purposes (annual means, hourly peaks). The models were tested under a year-long simulation. The dispersion from both a point and a volume source, belonging to an existing industrial plant, was analyzed separately. The inter-comparison was performed through the analysis of 2D ground concentration maps, scatterplots, and three classical indices from the 2D maps of annual concentration statistics. To correlate the differences among models with site characteristics, the statistics were analyzed not only globally, but also according to distance from the source, the elevation, and the land-use classification. The analysis shows that around-its-axis plume dispersion in LPM is lower than in PuM over all the land-use types except water surfaces, in agreement with the theoretical basis provided by the models. Because of its more advanced theoretical formulation, e.g., in the interaction of the plume with the complex terrain and the three-dimensional wind field, an LPM used as a comparison term allowed us to highlight the weaknesses of a more traditional approach, such as PuM, in reproducing effects such as plume up-sloping, deflection, channeling, confinement, and wind shear diffusion.

Southern Ocean Precipitation Characteristics Observed From CloudSat and Ground Instrumentation During the Macquarie Island Cloud &amp; Radiation Experiment (MICRE): April 2016 to March 2017

AbstractA 1‐year blended surface precipitation data set using Parsivel disdrometer, surface W‐band radar, and tipping bucket measurements is produced for the Macquarie Island Cloud and Radiation Experiment (MICRE) and compared with retrievals from CloudSat (spaceborne 94 GHz radar). Surface precipitation was observed 44% ± 4% of the time between April 2016 and March 2017. Precipitation composed primarily of small particles (diameter &lt;1 mm) occurred about 36% ± 2% of the time, constituting 10% of total accumulation. Remaining precipitation contained enough large particles such that the disdrometer could be used to identify the precipitation type as rain, ice, snow or wet snow. Seasonal and annual statistics on frequency of occurrence and accumulation for each precipitation type observed during MICRE are presented. Most ice and mixed phase precipitation was shallow, originating at a height of 3 km or lower, and occurred most often when Macquarie Island was to the northwest of the nearest cyclonic low‐pressure center. In contrast, rain was more often deep and occurred most frequently when the island was to the southeast of cyclonic lows. A weak diurnal cycle in frequency and mean rate was present with a minimum between 12:00 and 14:00 local time and maximum between 03:00 and 06:00 local time. The CloudSat 2C‐Precip‐Column product missed the lightest precipitation (because the near‐surface reflectivity is &lt;−15 dBZ) and overestimated total liquid precipitation and occurrence of mixed phase precipitation, but captured reasonably well the distribution of rain rates for rates &gt;0.5 mm/hr.