Automatic Weather Stations Research Articles

Sap Flow Responses of the Endangered Species Juniperus drupacea Labill. to Environmental Variables in Parnon Mountain, Greece

In the face of ongoing climatic changes, understanding the species’ sap flow responses is of crucial importance for adaptation and resilience of ecosystems. This study investigated diurnal variability and radial sap flux density (Js) in a natural Juniperus drupacea forest on Mt Parnon and determined the climatic factors affecting its total sap flow (Qs). Between July 2021 and March 2022, Granier-type sensors and automatic weather stations monitored Js of J. drupacea trees and environmental factors. Utilizing a multi-point sensor for Js radial profile variability, correction factors were applied to calculate (Qs), ranging from 4.78 to 16.18 L day−1. In drier months of the study period (July–September), Qs progressively increased with increasing PAR and soil temperature, reaching a plateau at maximum values (app. 600 µmol m−2 s−1 and 26 °C respectively) indicating partial stomatal closure. Whereas, during the wetter period (October–March), when water was no longer a limiting factor, VPD and PAR emerged as significant controllers of stand transpiration. In this period, Qs responded positively to increasing soil water content (θ) only on days with high VPD (>0.5 kPa). The studied J. drupacea stand demonstrated adaptability to varying environmental conditions, crucial for the species’ survival, considering anticipated climate change scenarios.

Developing a statistical approach of evaluating daily maximum and minimum temperature observations from third‐party automatic weather stations in Australia

AbstractThird‐party automatic weather stations (TPAWS) provide a compelling data source for scientists and practitioners to observe and estimate more accurate fine‐scale atmospheric conditions, including daily maximum and minimum temperature (denoted as Tmax and Tmin, respectively), than the current primary weather observation network can offer. Several uncertainties and errors arise in data from TPAWS as the quality control applied to these stations may be inadequate or ad hoc. In this study, we develop a statistical approach to evaluate the quality of daily Tmax and Tmin observations collected from TPAWS in Australia. Our approach compares a target observation with multiple types of reliable reference data, including neighbouring primary weather observations from the official Bureau of Meteorology of Australia stations, Australian Gridded Climate Data, and numerical weather prediction data. Guided by the operational requirements in terms of automation, interpretability, and simplicity as well as expandability, a separate test is formed for each type of reference data and then all the individual tests are merged to generate a single result based on a Gaussian mixture model that is used to provide the final overall assessment for each TPAWS observation. The overall assessment is made in the form of a p‐value‐based confidence score that measures the difference between the target observation and trusted reference data. Our method is validated by synthetic datasets based on high‐quality observations and is also applied to daily Tmax and Tmin observations from 184 TPAWS owned by the Department of Primary Industries and Regional Development of Western Australia. The framework can be readily applied to different regions with different reliable or trusted data sources.

The Impact of Climate Change on Socio-Economic and Migration as Adapted Coping Strategies in the Eastern Agro-Climatic Zone of Afghanistan

Climate change migration has become one of the main challenges in 21st century. Afghanistan is one of the most vulnerable countries in the world due to climate induces in the last three decades. The present study objective to find how the climate change impact on the socio-economic and migration as adapted coping strategies in eastern agro-climatic zone of Afghanistan. The study area was non-probability selected and data were collected from three provinces, Nangarhar, Kunar and Laghman out of four provinces according the sample size calculation method 384 sample respondents were face to face interviewed through a structured developed questionnaire. The result of the study showed that socio-economic impact of climate change about 60 percent of respondents say from high to a very high impact on health, agricultural products, opportunity, migrations, and reduction of household income. The results discovered that the sampled respondents among the eight coping strategies internal displacement as the main coping strategy, following the change in cropping pattern, new bore well and migrating children to another country as adapted strategies to mitigate climate variability in the eastern agro-climatic zone of Afghanistan. The study suggests that the government should develop new strategies for natural resources management, construct water dams, create a centralized system for climate data, adoption of technological innovation, and weather-based crop insurance, establish automatic weather stations and increase investment in sustainable agriculture to mitigate the climate change impact in the future.

The application and modification of WRF-Hydro/Glacier to a cold-based Antarctic glacier

Abstract. The McMurdo Dry Valleys (MDV) are home to a unique microbial ecosystem that is dependent on the availability of freshwater. This is a polar desert and freshwater originates almost entirely from surface and near-surface melt of the cold-based glaciers. Understanding the future evolution of these environments requires the simulation of the full chain of physical processes from net radiative forcing, surface energy balance, melt, runoff and transport of meltwater in stream channels from the glaciers to the terminal lakes where the microbial community resides. To establish a new framework to do this, we present the first application of WRF-Hydro/Glacier in the MDV, which as a fully distributed hydrological model has the capability to resolve the streams from the glaciers to the bare land that surround them. Given that meltwater generation in the MDV is almost entirely dependent on small changes in the energy balance of the glaciers, the aim of this study is to optimize the multi-layer snowpack scheme that is embedded in WRF-Hydro/Glacier to ensure that the feedbacks between albedo, snowfall and melt are fully resolved. To achieve this, WRF-Hydro/Glacier is implemented at a point scale using automatic weather station data on Commonwealth Glacier to physically model the onset, duration and end of melt over a 7-month period (1 August 2021 to 28 February 2022). To resolve the limited energetics controlling melt, it was necessary to (1) limit the percolation of meltwater through the ice layers in the multi-layer snowpack scheme and (2) optimize the parameters controlling the albedo of both snow and ice over the melt season based on observed spectral signatures of albedo. These modifications enabled the variability of broadband albedo over the melt season to be accurately simulated and ensured that modelled surface and near-surface temperatures, surface height change and runoff were fully resolved. By establishing a new framework that couples a detailed snowpack model to a fully distributed hydrological model, this work provides a stepping stone to model the spatial and temporal variability of melt and streamflow in the future, which will enable some of the unknown questions about the hydrological connectivity of the MDV to be answered.

On the Human Thermal Load in Fog

We characterized the thermal load of a person walking and/or standing in the fog by analyzing the thermal resistance of clothing, rcl, and operative temperature, To. The rcl–To model applies to individuals using weather data. The body mass index and basal metabolic flux density values of the person analyzed in this study are 25 kg m−2 and 40 W m−2, respectively. Weather data are taken from the nearest automatic weather station. We observed 146 fog events in the period 2017–2024 in Martonvásár (Hungary’s Great Plain region, Central Europe). The main results are as follows: (1) The rcl and To values were mostly between 2 and 0.5 clo and −4 and 16 °C during fog events, respectively. (2) The largest and smallest rcl and To values were around 2.5 and 0 clo and −7 and 22 °C, respectively. (3) The rcl differences resulting from interpersonal and wind speed variability are comparable, with a maximum value of around 0.5–0.7 clo. (4) Finally, rcl values are significantly different for standing and walking persons. At the very end, we can emphasize that the thermal load of the fog depends noticeably on the person’s activity and anthropometric characteristics.

Near-Surface Air Temperature Records over the Past 30 Years in the Interior of Dronning Maud Land, East Antarctica

Abstract The interior of Dronning Maud Land (DML) in East Antarctica is one of the most data-sparse regions of Antarctica for studying climate change. A monthly mean near-surface temperature dataset for the last 30 years has been compiled from the historical records from automatic weather stations (AWSs) at three sites in the region (Mizuho, Relay Station, and Dome Fuji). Multiple AWSs have been installed along the route to Dome Fuji since the 1990s, and observations have continued to the present day. The use of passive-ventilated radiation shields for the temperature sensors at the AWSs may have caused a warm bias in the temperature measurements, however, due to insufficient ventilation in the summer, when solar radiation is high and winds are low. In this study, these warm biases are quantified by comparison with temperature measurements with an aspirated shield and subsequently removed using a regression model. Systematic error resulting from changes in the sensor height due to accumulating snow was insignificant in our study area. Several other systematic errors occurring in the early days of the AWS systems were identified and corrected. After the corrections, multiple AWS records were integrated to create a time series for each station. The percentage of missing data over the three decades was 21% for Relay Station and 28% for Dome Fuji. The missing rate at Mizuho was 49%, more than double that at Relay Station. These new records allow for the study of temperature variability and change in DML, where climate change has so far been largely unexplored. Significance Statement Antarctic climate change has been studied using temperature data at staffed stations. The staffed stations, however, are mainly located on the Antarctic Peninsula and in the coastal regions. Climate change is largely unknown in the Antarctic plateau, particularly in the western sector of the East Antarctic Plateau in areas such as the interior of Dronning Maud Land (DML). To fill the data gap, this study presents a new dataset of monthly mean near-surface climate data using historical observations from three automatic weather stations (AWSs). This dataset allows us to study temperature variability and change over a data-sparse region where climate change has been largely unexplored.

Adoption of irrigation scheduling in the South African sugarcane industry: the use of a demonstration trial to bring about change

In the South African sugarcane industry, as in many countries, poor adoption of irrigation scheduling is common, leading to ineffective use of irrigation water and loss in yield and income. The reasons for the lack of adoption include economic constraints, difficulty with implementation and human perceptions. To demonstrate the benefits of effective irrigation scheduling under local conditions, a demonstration trial was conducted over 5 years at Pongola, South Africa. Here, a fixed irrigation schedule (control) was compared with three scheduling treatments: (1) the MyCanesim® computer model with data from an automatic weather station; (2) soil-water data from a capacitance probe; and (3) a combination treatment, where MyCanesim® was integrated with a capacitance probe. The number of stress days from either under- or over-irrigation, along with the RV (recoverable value) yield, irrigation applied, irrigation water productivity (IWP) and financial benefits was used to assess each treatment. The adoption of irrigation scheduling was also evaluated through farmer surveys before and after the trial. When compared to the fixed-cycle control, the use of irrigation-scheduling techniques resulted in an increase of up to 13% in RV yield, a reduction of up to 58% in irrigation water applied, improved IWP of up to 23.9 t cane/100 mm of irrigation and financial benefits of up to ZAR10,487/(ha · a) (USD698/(ha · a)). The combination treatment incurred the least water-stress days, 79% lower than the control. Farmer surveys showed an increase of 45% in the adoption of irrigation scheduling, from 11% (n = 111) in 2014 to 56% (n = 100) in 2020. Although site-specific, the trial results show substantial benefits that can be realised by scheduling irrigation. The trial results compare well to other similar studies, showing that the results can be used in other areas to promote the adoption of irrigation scheduling.

Long-term exposure to high perceived temperature and risk of mortality among patients with chronic kidney disease

Health risks due to climate change are emerging, particularly from high-temperature exposure. The perceived temperature is an equivalent temperature based on the complete heat budget model of the human body. Therefore, we aimed to analyze the effect of perceived temperature on overall mortality among patients with chronic kidney disease. In total, 32,870 patients with chronic kidney disease in Seoul participated in this retrospective study (2001–2018) at three medical centers. The perceived temperature during the summer season was calculated using meteorological factors, including the air temperature near the automated weather station, dew point temperature, wind velocity, and total cloud amount. We assessed the association between perceived temperature using Kriging spatial interpolation and mortality in patients with CKD in the time-varying Cox proportional hazards model that was adjusted for sex, age, body mass index, hypertension, diabetes mellitus, estimated glomerular filtration rate, smoking, alcohol consumption, and educational level. During the 6.14 ± 3.96 years of follow-up, 3863 deaths were recorded. In multivariable analysis, the average level of perceived temperature and maximum level of perceived temperature demonstrated an increased risk of overall mortality among patients with chronic kidney disease. The concordance index for mortality of perceived temperature was higher than temperature, discomfort index, and heat index. When stratified by age, diabetes mellitus, and estimated glomerular filtration rate, patients with chronic kidney disease with young age (age <65 years) showed higher hazard ratio for mortality (interaction P = 0.049). Moreover, the risk of death in the winter and spring seasons was more significant compared to that of the summer and autumn seasons. Therefore, long-term exposure to high perceived temperature during summer increases the risk of mortality among patients with chronic kidney disease.

Primary Impact Evaluation of Surface Temperature Observations for Microwave Temperature Sounding Data Assimilation over Land

Observations from the Advanced Microwave Sounding Unit-A (AMSU-A) onboard polar-orbiting satellites are considered to be the most effective satellite data in terms of obviously reducing operational prediction errors. However, there are still significant difficulties in the application of AMSU-A low-level channel data assimilation over land. One of them is the inaccurate surface skin temperature (SKT) of the background on land areas, which leads to significant uncertainty in the accuracy of simulating brightness temperature (BT) in these channels. Therefore, improving the accuracy of SKT in the background field is a direct way to improve the assimilation effect of these low-level channel data over land. In this study, both high-spatio-temporal-resolution automatic weather station (AWS) observation data from China in September 2021 and the AMSU-A observation data from NOAA-15/18/19 and MetOp-A were used. Based on the Advanced Research version of the Weather Research and Forecast model (WRF-ARW) and Gridpoint Statistical Interpolation (GSI) assimilation system, we first analyzed the differences in SKT between AWS observations and model simulations and then attempted to directly replace the simulated SKT with the observation data. On this basis, the differences in BT simulation effects over the land area of Southwest China before and after replacement were meticulously analyzed and compared. In addition, the impacts of SKT replacement in areas with different terrain elevations and in cloudy areas were also evaluated. The results indicate that the SKTs of background fields were generally lower than the surface observations, whereas the diurnal variation in SKT was not well simulated. After replacing the SKT of the background field with station observations, the BT differences between the observation and background (O–B, observation minus background) were remarkably reduced, especially for channels 3–5 and 15 of the AMSU-A. The volume of data passing the GSI quality control significantly increased, and the standard deviation of O–B decreased. Further analysis showed that the improvement effect was better in areas at an elevation above 1600 m. Moreover, introducing SKT observations leads to a significant and stable improvement over BT simulations in cloudy areas over land.

Rainfall variability in Indonesia new capital associated with the Madden-Julian Oscillation and its contribution to flood events

Accurate and current rainfall analysis is crucial for planning and development in the new capital city of Indonesia (IKN). This study examines the effect of the Madden-Julian Oscillation (MJO) on rainfall variability in IKN using a 20-year dataset of Integrated Multi-Satellite Retrievals for GPM (IMERG) version 6 and automatic weather station (AWS) data. The study analyzes flood events in IKN by examining flood information provided by the National Agency for Disaster Countermeasures (BNPB) from 2008 to 2022 and the contribution of the MJO to these events. The findings indicate that the MJO significantly influences rainfall variability in IKN, with a more pronounced effect during the dry season (JJASO) than in the wet season (NDJFMAM). This is demonstrated by the higher occurrence of wet days and increased daily rainfall intensity, which is linked to extreme rainfall and longer-duration events, mainly between midnight and morning (0400–0600 LST). Notably, although floods are more common during the rainy season, the MJO can intensify flood events in IKN during the dry season. Therefore, when developing an effective flood disaster mitigation system for IKN, it is crucial to consider the amplitude and phase of the MJO.

Analysis and Research on Data Anomaly of APOS300 Automatic Weather Station at Guiyang Airport

Analysis and Research on Data Anomaly of APOS300 Automatic Weather Station at Guiyang Airport

Risco climático para ocorrência da ferrugem asiática da soja no Mato Grosso, Rio Grande do Sul e Paraná

ABSTRACT Asian soybean rust is a primary factor affecting soybean production. Climate risk knowledge optimizes agricultural management, reduces pesticide use, and promotes environmental sustainability. The objective of this study was to evaluate the climatic risk of the occurrence of Asian soybean rust in the states of Mato Grosso, Rio Grande do Sul, and Paraná, Brazil, verifying the months with temperature and relative air humidity that are more favorable for disease development. Hourly meteorological data were obtained from the National Institute of Meteorology, collected from 36 automatic weather stations in Mato Grosso state, 44 in Rio Grande do Sul state, and 26 in Paraná state, from the beginning of the operation until December 31, 2021. The climatic risk values for Asian soybean rust occurrence were calculated daily, obtained by the product of the response functions to leaf wetness duration and air temperature during this period. The average total climatic risk of Asian soybean rust occurrence in Mato Grosso, Rio Grande do Sul, and Paraná states, was 44, 30, and 36%, respectively. Relative air humidity was the meteorological element with the greatest climatic risk for the development of the disease in the states of Rio Grande do Sul and Paraná, Brazil, and air temperature was the most limiting, the opposite was observed for the state of Mato Grosso, where air temperature was favorable and relative air humidity was more limiting.

Trends in the Air Temperature: A Practical Approach for Auto‐ and Cross‐Correlation Analysis

We study the air temperature with 10 years of hourly measured in 12 automatic weather stations, located in the State of Bahia (Brazil), considering mainly its auto‐ and cross‐correlation. We found that, in general, there is a positive linear trend, indicating warming in this variable for all stations. Also, we observe that the probability distribution function clearly exhibits a bimodal distribution, meaning that there are two main seasonal cycles (the largest relating to winter, the other to summer). In order to have a more robust statistical analysis, detrended fluctuation analysis (DFA) method was applied in these time‐series to analyzing data where traditional methods might struggle due to nonstationary. In this case, DFA was useful in identifying long‐range autocorrelations in these time‐series (with persistent and antipersistent behavior) with specific seasonal time‐scales (1 day and 1 year). However, if we quantify the cross‐correlations between the air temperature in Salvador and the other stations, by ρDCCA, we found that detrended cross‐correlation analysis cross‐correlation coefficient is always positive with two peaks, the first is located at n = 24 (1 day) and the second at n = 8766 (1 year). In this sense, ρDCCA coefficient provides insights how air temperature in Salvador is correlated with air temperature in other weather stations over different time‐scales, helping to understand their long‐term interactions. These results, for example, allow us to identify temporal scales that may be useful to better understand trends in the air temperature and that can help us further answer global warming questions.

Pengembangan Sistem Prediksi Waktu Penyiraman Optimal pada Perkebunan: Pendekatan Machine Learning untuk Peningkatan Produktivitas Pertanian

Modern agriculture relies heavily on technology, especially in irrigation management and crop watering. Several previous studies have applied field data-based predictive techniques to improve crop yields. This research aims to develop a prediction system for optimal watering time in plantations and agriculture using a machine learning approach. The rigorous methodology includes data capture, pre-processing, model evaluation and testing, validation, and visualization. High accuracy demonstrates the system's reliability in determining optimal watering needs to improve resource efficiency and crop yields in agriculture. The data obtained from the automatic weather station (AWS) via thingsboard is processed sequentially, starting from data retrieval in json format using postman to transformation into csv files with proper timestamp adjustment. The pre-processing stage includes data cleaning, variable selection, data integration, and generating a clean dataset. In the evaluation stage, the dataset is divided into training data and test data, with the application and comparison of logistic regression, random forest and decision tree models applied as classifiers. Furthermore, the validation and results stage includes prediction, performance testing using the confusion matrix, and visualization of prediction results in the form of text and icons that aim to increase interpetability for users through Google Collaboratory. The results of this research provide an overview of the optimal watering time based on the dataset from the automatic weather station. Further analysis shows that the implementation of machine learning models significantly improves the prediction accuracy, proving the effectiveness of the system in providing more precise watering time recommendations to increase agricultural productivity. The main objective is to develop a machine learning-based watering time prediction system using data from the automatic weather station and evaluate various classifier algorithms to select the best model.

Solar Radiation Intensity Imputation in Pyranometer of Automatic Weather Station Based on Long Short Term Memory

Automatic Weather Station (AWS) experienced problems in the form of component damage and communication system failure, resulting in incomplete parameter data. Component damage also occurs in pyranometers. Decreased pyranometer performance results in deviations, uncertainty in measuring solar radiation intensity, and data gaps. Data imputation is one solution to minimize measurement deviations and the occurrence of missing AWS pyranometer data. This research aims to design and analyze the accuracy performance of the multisite AWS pyranometer solar radiation intensity data imputation model when a data gap occurs. This research attempts to utilize the spatio-temporal relationship of multisite AWS solar radiation intensity in the imputation model. Long-Short Term Memory (LSTM) algorithm is used as an estimator in the multisite AWS pyranometer network. Data imputation modeling stage includes data collection, data pre-processing, creating missing data scenarios, LSTM design and model testing. Overall, LSTM-based imputation model has ability of filling gap data on AWS Cikancung pyranometer with maximum missing sequence of 12 hours. Imputation model has MAPE 1.76% - 5.26% for missing duration 30 minutes-12 hours. It still it meet WMO requirement for solar radiation intensity measurement with MAPE&lt;8%.

Predictive Maintenance Automatic Weather Station Sensor Error Detection using Long Short-Term Memory

Weather information plays a crucial role in various sectors due to Indonesia's wide range of weather and extreme climate phenomena. Automatic Weather Stations (AWS) are automated equipment designed to measure and collect meteorological parameters such as atmospheric pressure, rainfall, relative humidity, atmospheric temperature, wind speed, and wind direction. Occasionally, AWS sensors may produce erroneous values without the technicians' awareness. This study aims to develop sensors error detection system for predictive maintenance on AWS using the Long Short-Term Memory (LSTM) model. The AWS dataset from Jatiwangi, West Java, covering the period from 2017 to 2021, will be utilized in the study. The study revolves around developing and testing four distinct LSTM models dedicated to each sensor: RR, TT, RH, and PP. The research methodology involves a phased approach, encompassing model training on 70% of the available dataset, subsequent validation using 25% of the data, and finally, testing on 5% of the dataset alongside the calibration dataset. Research outcomes demonstrate notably high accuracy, exceeding 90% for the RR, TT, and PP models, while the RH model achieves above 85%. Moreover, the research highlights that Probability of Detection (POD) values generally trend high, surpassing 0.8, with a low False Alarm Rate (FAR), typically below 0.1, except for the RH model. Sensor condition requirements will adhere to the rules set by World Meteorological Organization (WMO) and adhere to the permitted tolerance limits for each weather parameter.

Refractivity Observations from Radar Phase Measurements: The 22 May 2002 Dryline Case during IHOP Project

The dryline, often associated with the development of severe storms in the Southern Great Plains of the United States of America, is a boundary layer phenomenon that occurs when a warm and moist air mass from the Gulf of Mexico meets a hot and dry air mass from the southwest desert area. An accurate knowledge of the water vapor spatio-temporal variability in the lower part of the atmosphere is crucial for a better understanding of the evolution of the dryline. The tropospheric refractivity, directly related to water vapor content, is a proxy for the water vapor content of the troposphere. It has already been demonstrated that the refractivity and the refractivity vertical gradient can be jointly estimated from radar phase measurements. In fact, it has been shown that using kriging interpolation techniques, accurate refractivity maps within the coverage area of the radar can be obtained with high temporal resolution. In this paper, a detailed analysis of the time series of radar-based refractivity maps obtained during a dryline that occurred on the afternoon of 22 May 2002 during the International H2O Project (IHOP_2002) is presented. Comparisons between the time series of radar refractivity maps, obtained with the NCAR S-Pol radar, and the refractivity measurements derived from automatic ground-based weather stations and the AERI instrument, placed at different locations within the coverage area of the NCAR S-Pol radar, demonstrate the accuracy of radar refractivity estimates even for highly variable conditions, both in time and space, in the troposphere. Correlation coefficients higher than 0.95 are obtained in all weather station locations. Regarding the RMSE, errors less than 6 N-units are obtained for all cases, being even as low as 2.92 N-units at some locations.

Spatial Estimation of Snow Water Equivalent for Glaciers and Seasonal Snow in Iceland Using Remote Sensing Snow Cover and Albedo

Efficient water resource management in glacier- and snow-dominated basins requires accurate estimates of the snow water equivalent (SWE) in late winter and spring and melt onset timing and intensity. To understand the high spatio-temporal variability of snow and glacier ablation, a spatially distributed energy balance model combining satellite-based retrievals of albedo and snow cover was applied. Incoming short-wave energy, contributing to daily estimates of melt energy, was constrained by remotely sensed surface albedo for snow-covered surfaces. Fractional snow cover was used for non-glaciated areas, as it provides estimates of snow cover for each pixel to better constrain snow melt. Thus, available daily estimates of melt energy in a given area were the product of the possible melt energy and the fractional snow cover of the area or pixel for non-glaciated areas. This provided daily estimates of melt water to determine seasonal snow and glacier ablation in Iceland for the period 2000–2019. Observations from snow pits on land and glacier summer mass balance were used for evaluation, and observations from land and glacier-based automatic weather stations were used to evaluate model inputs for the energy balance model. The results show that the interannual SWE variability was generally high both for seasonal snow and glaciers. For seasonal snow, the largest SWE (&gt;1000 mm) was found in mountainous and alpine areas close to the coast, notably in the East- and Westfjords, Tröllaskaga, and in the vicinity of glacier margins. Lower SWE values were observed in the central highlands, flatter inland areas, and at lower elevations. For glaciers, more SWE (glacier ablation) was associated with lower glacier elevations while less melt was observed at higher elevations. For the impurity-rich bare-ice areas that are exposed annually, observed SWE was more than 3000 mm.

Hindcast Insights from Storm Surge Forecasting of Super Typhoon Saola (2309) in Hong Kong with the Sea, Lake and Overland Surges from Hurricanes Model

Super Typhoon Saola (2309) skirted past south-southeast of Hong Kong within 40 km on the night of 1 September 2023, posing a significant storm surge threat to Hong Kong. Given the close proximity of Saola with a peak intensity of about 210 km/h within 300 km of Hong Kong, a close call of the “super typhoon direct-hit” scenario, this case provides valuable insights from a hindcast review of storm surge forecasts and warning operation using the Sea, Lake and Overland Surges from Hurricanes (SLOSH) model, which is the operational storm surge model adopted by the Hong Kong Observatory (HKO). The performance of the HKO’s PRobabilistic Inundation Map Evaluation System (PRIMES) using both statistical and model ensemble approaches was also reviewed in this paper. Saola was a challenging case for operational forecasting of a compact TC structure with changes in storm size and intensity when it came close to Hong Kong. With major observations of storm structure using weather radar and dense automatic weather station, tide gauge and water level gauge networks, the high sensitivity of storm surge forecasts to the storm size parameter and the distance of closest approach was clearly revealed in the case of Saola. Even with a circularly symmetric TC parametric model like SLOSH, the hindcast review results illustrated that the model outputs were reasonably accurate during the closest approach of Saola given an accurate storm size and distance of closest approach were input, and using a highly computationally efficient storm surge model made it possible for the nowcasting of storm surges to handle compact and intense TC direct-hit cases in operational TC forecasting. Taking a nowcasting approach not only helps provide more reliable storm tide forecasts, but also facilitates the formulation of a better warning strategy when making final-call decisions in emergency response actions, based on the more frequent real-time analysis of TC position, intensity and storm size and the more accurate prediction of these parameters. A nowcasting workflow for storm surge operation was proposed in this paper.

Simulations of the human heat balance during Mount Everest summit attempts in spring and winter

The majority of research dealing with the impacts of the Himalayan climate on human physiology focuses on low air temperature, high wind speed, and low air pressure and oxygen content, potentially leading to hypothermia and hypoxia. Only a few studies describe the influence of the weather conditions in the Himalayas on the body’s ability to maintain thermal balance. The aim of the present research is to trace the heat exchange between humans and their surroundings during a typical, 6-day summit attempt of Mount Everest in the spring and winter seasons. Additionally, an emergency night outdoors without tent protection is considered. Daily variation of the heat balance components were calculated by the MENEX_HA model using meteorological data collected at automatic weather stations installed during a National Geographic expedition in 2019–2020. The data represent the hourly values of the measured meteorological parameters. The research shows that in spite of extreme environmental conditions in the sub-summit zone of Mount Everest during the spring weather window, it is possible to keep heat equilibrium of the climbers’ body. This can be achieved by the use of appropriate clothing and by regulating activity level. In winter, extreme environmental conditions in the sub-summit zone make it impossible to maintain heat equilibrium and lead to hypothermia. The emergency night in the sub-peak zone leads to gradual cooling of the body which in winter can cause severe hypothermia of the climber’s body. At altitudes < 7000 m, climbers should consider using clothing that allows variation of insulation and active regulation of their fit around the body.