Observation Stations Research Articles

Effects of Aquatic Physicochemical Parameters Variation on the Phytoplankton Abundance and Diversity in the Babon River, (Semarang, Central Java, Indonesia)

The Babon River is an integral component of Semarang City's drainage system, which is heavily impacted by the influx of waste, leading to elevated water temperatures caused by global warming and decreased pH levels due to acid rain. The two main environmental factors that affect phytoplankton growth and productivity are temperature and nutrient availability. Nutrients can change the balance of the natural food webs of aquatic ecosystems. Meanwhile, metals are toxic and can accumulate in biota tissues. This study examined how nutrients, pH, temperature, and heavy metals in water affected the diversity and abundance of phytoplankton. Water samples and phytoplankton were collected from 7 observation stations representing the upstream, middle, and downstream rivers. Observations at each station were made 3 times in the months of April-May, June-July, and August-September. Temperature and pH were measured in situ with the HORIBA Water Checker. Analysis of nitrate, phosphate, and heavy metals was carried out with an AAS. The results showed that the dominant phytoplankton in the Babon Rivers consisted of Bacillariophyceae, Chlorophyceae, and Cyanophyceae. There is no diversity against all independent variables (temperature, pH, nitrate, phosphate, Cd, Total Cr, Pb concentrations) and dependent variables (number of genera, abundance, and diversity index of phytoplankton) due to the sampling station's location. However, sampling time caused diversity in nitrate, phosphate, Cd, Total Cr, and Pb concentrations, the sampling location effect was more dominant than sampling time, so the results showed that all independent variables did not contribute significantly to the dependent variables.

Community Structure, Abundance and Ecological Index of Phytoplankton in Jembrana Coastal Waters, Bali

Phytoplankton is an important component in aquatic ecosystems. Apart from producing oxygen, phytoplankton also act as primary producers in the food chain. Bali Sardinella (Lemuru) is an important fishery commodity in the Bali Strait, which is known to eat plankton (Plankton Feeder), so its existence depends on the distribution of plankton. This descriptive research aims to determine the community structure, abundance and ecological index of phytoplankton in the coastal waters of Jembrana Regency. Sampling was conducted in July 2023 at five observation stations in the coastal waters of Jembrana Regency (Pengambengan Beach, Perancak Beach and Delod Berawah Beach). Around 250 litres of seawater was filtered by a plankton net measuring 35 mm. The filtered samples collected in a 100 ml collecting bottle were then transferred to a 120 ml sample bottle and preserved in 4% formalin. The samples obtained were taken to the laboratory for observation under a microscope. This research shows that 66 types of phytoplankton were found to belong to 5 classes in the waters of Jembrana, Bali. The Bacillariophyceae class were found with the highest number of species (44 species), followed by the Dinophyceae (19 species), Chlorophyceae (1 species), Cryptophyceae (1 species), and Cyanophyceae (1 species). Based on the results of calculating phytoplankton abundance, various abundance values ​​were obtained at each observation station. Station 2 has the highest abundance of phytoplankton with 297 individuals/l followed by Station 1 (221 individuals/l), Station 4 (130 individuals/l), Station 3 (127 individuals/l), and Station 5 (36 individuals/l). The species Diversity Index (H') ranges from 2.61 - 2.83, the Dominance Index (C) ranges from 0.07 - 0.13, and the Evenness Index (e) ranges from 0.72 - 0.95. The ecological index value shows that the phytoplankton community in Jembrana coastal waters included in the moderate diversity category is not dominant and evenly distributed. The ecological index value shows that the water quality is categorized as moderately polluted.

Assimilation of ground-based GNSS data using a local ensemble Kalman filter

Tropical cyclones become increasingly nonlinear and dynamically unstable in high-resolution models. The initial conditions are typically sub-optimal, leaving scope to improve the accuracy of forecasts with improved data assimilation. Simultaneously, the lack of real ground-based GNSS observations over the ocean poses significant challenges when evaluating the assimilation results in oceanic regions. In this study, an Observation System Simulation Experiment is carried out based on a tropical cyclone case. Assimilation experiments using the WRF-PDAF framework are conducted. Conventional and GNSS observation operators are implemented. A diverse array of synthetic observations, encompassing temperature (T), wind components (U and V), precipitable water (PW), and zenith total delay (ZTD), are assimilated utilizing the Local Error-Subspace Transform Kalman filter (LESTKF). The findings highlight the improvement in forecast accuracy achieved through the assimilation process over the ocean. Multiple observation types further improve the forecast accuracy. The study underscores the crucial role of GNSS data assimilation techniques. The assimilation of GNSS data presents potential for advancing weather forecasting capabilities. Thus, the construction of ground-based GNSS observation stations over the ocean is promising.

Near-Surface Air Temperature Estimation Based on an Improved Conditional Generative Adversarial Network.

To address the issue of missing near-surface air temperature data caused by the uneven distribution of ground meteorological observation stations, we propose a method for near-surface air temperature estimation based on an improved conditional generative adversarial network (CGAN) framework. Leveraging the all-weather coverage advantage of Fengyun meteorological satellites, Fengyun-4A (FY-4A) satellite remote sensing data are utilized as conditional guiding information for the CGAN, helping to direct and constrain the near-surface air temperature estimation process. In the proposed network model of the method based on the conditional generative adversarial network structure, the generator combining a self-attention mechanism and cascaded residual blocks is designed with U-Net as the backbone, which extracts implicit feature information and suppresses the irrelevant information in the Fengyun satellite data. Furthermore, a discriminator with multi-level and multi-scale spatial feature fusion is constructed to enhance the network's perception of details and the global structure, enabling accurate air temperature estimation. The experimental results demonstrate that, compared with Attention U-Net, Pix2pix, and other deep learning models, the method presents significant improvements of 68.75% and 10.53%, respectively in the root mean square error (RMSE) and Pearson's correlation coefficient (CC). These results indicate the superior performance of the proposed model for near-surface air temperature estimation.

Computer Vision Based Areal Photographic Rocket Detection using YOLOv8 Models

Advances in aerospace engineering and aerodynamics have pioneered space exploration and helped support telecommunication infrastructure. But these same developments have also aided in the creation of weapons of devastating impact. This necessitates the development of ways for detecting and tracking rockets. While several methods, mostly based on Doppler radar exist, the need for active radio emissions limits the applicability of these systems. A passive system has several advantages over traditional techniques, however their potential is largely unexplored. This work seeks to tackle this research gap by exploring the potential of emerging computer vision townies applied to rocket detection and tracking. The advantages of such a system are the relatively low cost as well as passive nature making observation stations harder to detect and easier to deploy. This work explores the potential of pre-trained, lightweight YOLOv8 architectures for rocket detection in real-world situations. A publicly available dataset is utilized and a comparative analysis is carried out between nano and small models. Both models demonstrate favorable outcomes with an accuracy of 0.90 for rocket body detection and 0.93 for engine flame detection. Nevertheless, rocket detection into space is still difficult, with a precision of 0.64 for this class. This paper indicates areas for additional refinement and demonstrates the potential of computer vision technology in passive rocket detection.

Time‐series‐based ensemble model output statistics for temperature forecasts postprocessing

AbstractThe uncertainty in numerical weather prediction models is nowadays quantified by the use of ensemble forecasts. Although these forecasts are continuously improved, they still suffer from systematic bias and dispersion errors. Statistical postprocessing methods, such as the ensemble model output statistics (EMOS), have been shown to substantially correct the forecasts. This work proposes an extension of EMOS in a time‐series framework. Besides taking account of seasonality and trend in the location and scale parameter of the predictive distribution, the autoregressive process in the mean forecast errors or the standardized forecast errors is considered. The models can be further extended by allowing generalized autoregressive conditional heteroscedasticity. Furthermore, it is outlined how to use these models for arbitrary forecast horizons. To illustrate the performance of the suggested EMOS models in time‐series fashion, we present a case study for the postprocessing of 2 m surface temperature forecasts using five different lead times and a set of observation stations in Germany. The results indicate that the time‐series EMOS extensions are able to significantly outperform the benchmark models EMOS and autoregressive EMOS (AR‐EMOS) in most of the lead time–station cases.

Study of Heat Wave Using High‐Resolution Real Time Meso‐Scale Analysis Over India

AbstractThe applicability and accuracy of high‐resolution Real‐Time Meso‐scale Analysis (RTMA) system is assessed over India for the first time. The RTMA is a high‐spatial (2.5 km) and temporal resolution analysis system for near‐surface weather conditions. It is used to simulate near‐surface air temperature over India during the Heatwave (HW) period 12th to 20th April 2023. The verification analysis of temperature using the GLDAS gridded temperature shows reasonable improvement in the analysis from RTMA by capturing regional features compared to first‐guess. The spatial and temporal verification using the IMD station observations also confirms the value addition of RTMA in capturing the locations of recorded highest Tmax and their daily variations. The location‐specific heat stress analysis of RTMA shows high skill over many locations during HW days. Heat stress regions have been accurately brought out in the RTMA. Hence, the RTMA can be used for now‐casting and severe weather monitoring.

New Records of the Egg Development Phase of Varuna litterata in the Lower Serayu River, Central Java

One of the Brachyura crabs found in the lower reaches of the Serayu River is Varuna litterata, also known as the "herring bow crab". This crab usually inhabits shallow tidal areas and hides under rocks, logs, or dead leaves. It is a highly adapted crab found in a wide range of salinities. V. litterata is also a fishery product consumed in Thailand, the Philippines, and Indonesia. This research was conducted in the Lower Serayu River, Central Java, which consists of three observation stations, namely: 1) the first station is located above the dam with fresh water, 2) the second station is below the dam with fresh water properties and 3) the third station is downstream (estuary) of the Serayu River with brackish water. This research was conducted for four months, from October 2023 to January 2024. This study aimed to determine the level of egg development in V. litterata. The results showed that V. litterata experienced four stages in egg development and then hatched into zoea. At the initial development stage, V. litterata eggs will be purple; in the second stage, the eggs will turn reddish; in the third development stage, the eggs will turn orange to brownish; and in the fourth stage, the eggs will develop and become black. V. litterata that will spawn will migrate to brackish waters to hatch their eggs. V. litterata produces 20,708 - 85,886 eggs with an average egg diameter of 0.440 - 0.466 mm.

Characterization of extreme rainfall changes and response to temperature changes in Guizhou Province, China

Different geographical zones have regional heterogeneity in underlying earth surface structure and microclimate which result in different evolution trends and their response to climate change varies in extreme rainfalls in these zones. In the Guizhou province of China, there are complex landforms, which lead to spatial redistribution of rainfall, frequent extreme rainfall, and disasters high risk of geologic disasters. Research on extreme climate in Guizhou mostly paid attention to its spatio-temporal characteristics and modeling, but lack of analysis on its characteristics of extreme rainfall variability and response to temperature changes under different subsurface conditions. This study investigated the characteristics of the extreme rainfall spatiotemporal and recurrence periods in Guizhou province and discussed the relationship between the response of extreme rainfall to temperature change. Daily rainfall data from 1990 to 2020 and 2021–2100 at 31 meteorological observation stations throughout the province were collected to calculate extreme precipitation. This research had the following results. (1) Both historical and future periods show an upward trend in extreme rainfall in Guizhou province, with a spatial distribution pattern of “high in the south and low in the north, high in the east and low in the west” and “high in the southeast and low in the northwest”, respectively; the spatial distribution of extreme rainfall under each recurrence period is consistent with the non-recurrence period. (2) Both historical and future periods show an upward trend in temperature in Guizhou province, with a spatial distribution consistent with that of the extreme rainfall in the corresponding period. (3) The change in extreme rainfall intensity with increasing temperature is almost always greater than the C–C rate for different periods and underlying earth surface structure; Extreme rainfall has a Hook response structure to temperature change, and the climate response structure shifts to the right with climate warming. The results of the study can provide a basis for decision-making on regional disaster prevention and mitigation in the context of temperature change.

Improving GNSS-IR Sea Surface Height Accuracy Based on a New Ionospheric Stratified Elevation Angle Correction Model

Approximately 71% of the Earth’s surface is covered by vast oceans. With the exacerbation of global climate change, high-precision monitoring of sea surface height variations is of vital importance for constructing global ocean gravity fields and preventing natural disasters in the marine system. Global Navigation Satellite System Interferometry Reflectometry (GNSS-IR) sea surface altimetry is a method of inferring sea surface height based on the signal-to-noise ratio of satellite signals. It enables the retrieval of sea surface height variations with high precision. However, navigation satellite signals are influenced by the ionosphere during propagation, leading to deviations in the measured values of satellite elevation angles from their true values, which significantly affects the accuracy of GNSS-IR sea surface altimetry. Based on this, the contents of this paper are as follows: Firstly, a new ionospheric stratified elevation angle correction model (ISEACM) was developed by integrating the International Reference Ionosphere Model (IRI) and ray tracing methods. This model aims to improve the accuracy of GNSS-IR sea surface altimetry by correcting the ionospheric refraction effects on satellite elevation angles. Secondly, four GNSS stations (TAR0, PTLD, GOM1, and TPW2) were selected globally, and the corrected sea surface height values obtained using ISEACM were compared with observed values from tide gauge stations. The calculated average Root Mean Square Error (RMSE) and Pearson Correlation Coefficient (PCC) were 0.20 m and 0.83, respectively, indicating the effectiveness of ISEACM in sea surface height retrieval. Thirdly, a comparative analysis was conducted between sea surface height retrieval before and after correction using ISEACM. The optimal RMSE and PCC values with tide gauge station observations were 0.15 m and 0.90, respectively, representing a 20.00% improvement in RMSE and a 4.00% improvement in correlation coefficient compared to traditional GNSS-IR retrieval heights. These experimental results demonstrate that correction with ISEACM can effectively enhance the precision of GNSS-IR sea surface altimetry, which is crucial for accurate sea surface height measurements.

Predicting agricultural and meteorological droughts using Holt Winter Conventional 2D-Long Short-Term Memory (HW-Conv2DLSTM).

Drought is an extended shortage of rainfall resulting in water scarcity and affecting a region's social and economic conditions through environmental deterioration. Its adverse environmental effects can be minimised by timely prediction. Drought detection uses only ground observation stations, but satellite-based supervision scans huge land mass stretches and offers highly effective monitoring. This paper puts forward a novel drought monitoring system using satellite imagery by considering the effects of droughts that devastated agriculture in Thanjavur district, Tamil Nadu, between 2000 and 2022. The proposed method uses Holt Winter Conventional 2D-Long Short-Term Memory (HW-Conv2DLSTM) to forecast meteorological and agricultural droughts. It employs Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data precipitation index datasets, MODIS 11A1 temperature index, and MODIS 13Q1 vegetation index. It extracts the time series data from satellite images using trend and seasonal patterns and smoothens them using Holt Winter alpha, beta, and gamma parameters. Finally, an effective drought prediction procedure is developed using Conv2D-LSTM to calculate the spatiotemporal correlation amongst drought indices. The HW-Conv2DLSTM offers a better R2 value of 0.97. It holds promise as an effective computer-assisted strategy to predict droughts and maintain agricultural productivity, which is vital to feed the ever-increasing human population.

A dataset of meteorological observations at Jilin Da’an National Field Observation and Research Station of Agroecosystem from 2017 to 2021

A dataset of meteorological observations at Jilin Da’an National Field Observation and Research Station of Agroecosystem from 2017 to 2021

A dataset of soil water storage of typical ecosystems in arid and semi-arid areas in China, 2005-2017

Soil water is one of the important components of the earth's water resources, and also the main source of water directly consumed by vegetation and crops. Therefore, the storage, recharge, consumption, renewal and balance of soil water are of great significance to agriculture, animal husbandry, forestry, ecological environment and water resources balance, especially for the arid and semi-arid region of China. We collect and sort out the long-term observation data of soil moisture from eight ecosystem observation and research stations of the Chinese Ecosystem Research Network (CERN) located in the arid and semi-arid region of China during 2005 - 2017, and generates the corresponding soil water storage dataset through quality control, spatial and temporal normalization, statistical calculation, in order to provide long-term data support for understanding distribution and change of soil water storage in typical ecosystems in arid and semi-arid areas of China.

A dataset of meteorological observations at the National Field Scientific Observation and Research Station of Farmland Ecosystem in Changwu County, Shaanxi Province (2005–2019)

A dataset of meteorological observations at the National Field Scientific Observation and Research Station of Farmland Ecosystem in Changwu County, Shaanxi Province (2005–2019)

A dataset of soil nutrients from long-term farmland observation plots at Huanjiang Observation and Research Station for Karst Ecosystems, Chinese Academy of Sciences (2006–2022)

A dataset of soil nutrients from long-term farmland observation plots at Huanjiang Observation and Research Station for Karst Ecosystems, Chinese Academy of Sciences (2006–2022)

Case studies of the winds in the urban area of Hong Kong – Microclimate station observations and high resolution numerical simulations

Hong Kong, renowned for its densely packed urban areas, poses unique challenges for understanding the effects of buildings on local meteorological conditions. To address this, the Hong Kong Observatory has started building a network of urban meteorological monitoring stations since 2017 for monitoring, analysing and studying urban microclimate. This paper presents an observational and numerical study focusing on wind measurements obtained from wind sensors installed on two smart lampposts in Tsim Sha Tsui, a major urban area in Hong Kong. Two representative high wind conditions in Hong Kong, Super Typhoon Saola in 2023 and a strong monsoon case characterized by prevailing easterly winds, are considered. With the use of high resolution computational fluid dynamic simulations, major features of actual observations can be reproduced. This suggests that district scale or even street scale weather services could be possible in the future with sufficient computational power.

The Seasonal Variations of the Thermocline in the Banda Sea and its Water Mass Characteristics

This research was conducted to examine the seasonal variations in the Banda Sea's thermocline layer and its water mass characteristics. The research was conducted by analyzing Argo Float data. The data consists of six observation stations in West Season and Transitional Season II, and four stations in Transition Season I and East Season The thermocline layer thickness was identified based on the depth where the temperature decreased ≥ 0.05oC/m. The thermocline layer's distribution of depth and thickness and the distribution of temperature and salinity seasonally in the thermocline layer were analyzed with Microsoft Excel 2010. The analysis results showed that East Season and Transition Season II have a shallower upper boundary depth of the thermocline layer, a deeper lower boundary of the thermocline layer, and the thermocline layer is thicker when compared to the West Season and Transition Season I. The average temperature distribution is higher and the average salinity is lower in the West Season and Transition Season I. The mean rate of change in salinity and temperature in the thermocline layer seasonally ranged from 0.002-0.004 PSU/m and 0.06-0.08oC/m. The thermocline layer's stratification is stronger in the West Season and Transition Season I.

Hourly estimation of water reaching the ground surface in snow-covered regions of Japan using mesoscale meteorological data with the heat balance method

In snow-covered regions, landslides, including debris flows, frequently result from snowmelt and cause significant hazards. Because these snowmelt-induced landslides are triggered by increasing groundwater level and pore-water pressure acting on the slip surface due to infiltration of snowmelt, sometimes mixed with rainwater, accurate estimation of water reaching the ground surface (abbreviated MR in this study) on an hourly basis is essential for developing countermeasures such as an early warning system. This study introduces a model for estimating hourly MR, as well as snow water equivalent and snow depth, using mesoscale meteorological data provided by the Japan Meteorological Agency. The model considers four processes: precipitation, snow accumulation and densification, snowmelt, and infiltration. In the snowmelt process, snowmelt is estimated using the heat balance method. We applied the model to a snow observation station in Niigata Prefecture, central Japan, where MR, snow water equivalent, and snow depth were observed during the winter seasons of 2017–18 through 2020–21. Comparisons of observed and calculated values demonstrated that the model successfully reproduced the observations, regardless of the amount of snowfall in individual winter seasons. We propose that this model can estimate hourly MR, snow water equivalent, and snow depth at any location in snow-covered regions where on-site meteorological and snow observations are unavailable.

Possibilities of Using Regional Index-Flood Method and Annual Maximum and Partial Duration Series: A Case Study of Susurluk River Basin, Turkey

Floods are defined as disasters that occur as a result of a natural formation and cause negative effects on society life and economy. Floods that cause the greatest loss of life and property after earthquakes among the natural disasters experienced in our country, unplanned urbanization caused by the increasing population, uncontrolled settlements in river beds, and changing climate have increased its impact and frequency of occurrence over time. Therefore, it is important to estimate the magnitude of floods and the frequency of their occurrence in the most accurate way. This research aims to perform at-site and regional frequency analysis with L-moment methods in the Susurluk Basin. For regional frequency analysis, annual maximum and partial duration flood series were obtained using the data from 22 flow observation stations selected from the basin. In the first stage, the stations in the region were considered as a whole and the stations were found discordant with each other. Hierarchical cluster analysis was performed with the help of various physiological-hydrological characteristics of the stations located in the basin with geographic information systems, and the basin was divided into 2 regions, but homogeneity was still not achieved. Therefore, the basin is divided into 3 regions according to the dendrogram. There was no discordancy in all three regions and homogeneity was achieved. After achieving homogeneity, the most suitable frequency distribution was determined for each region. In addition, L-moments and L-moment ratios, probability distributions, and quantile discharges of the annual maximum and partial duration flood series were estimated for at-site frequency analysis. As a result, in regional flood frequency analysis, generalized logistic distribution provided the best fit in the annual maximum series, while Generalized Pareto and Pearson type 3 provided the best-fit distributions for the first and second regions in the partial duration series, respectively, in addition, generalized normal provided the best fit in the third region. It was concluded that it would be useful to use partial duration flood series as well as annual maximum series in the flood frequency analysis of the Susurluk basin.

FACTOR FOR CORRECTING THE RAINFALL OF CHIRPS SATELLITE DATA AGAINST OBSERVATION DATA ON THE CILIWUNG WATERSHED(CASE STUDY OF KEMAYORAN METEOROLOGI STATION)

The hydrological and environmental cycles in a river area strongly affect rainfall intensity and seasonal patterns. To accurately assess water resource capacity, precise rainfall data from each observation station is crucial. However, unevenly distributed rain gauges often challenge researchers, as insufficient data can hinder their analysis. In these situations, satellite images can provide valuable additional information.Aims: The objective of this study was to analyze the accuracy of CHIRPS satellite rainfall data from observation stations in the Ciliwung watershed, especially in the DKI Jakarta Province area, over the last 30 years (1993–2022).Methodology and results: Statistical analysis such as multiple linear regression with the stepwise method is used to analyze CHIRPS rainfall against observed rainfall data according to the location of the rain station. The validation results in this study show that the average results of the two observation stations have a value of R2 = 0.91 and NSE = 0.9068.Conclusion, significance and impact study: CHIRPS data can be categorized as very good if used as an alternative to limited observational rainfall data, which can then be used in analyzing water availability in the Ciliwung watershed (Jakarta).