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- Temperature Anomalies in the Northern and Southern Hemispheres: Evidence
of Persistence and Trends
Abstract: This paper analyses temperature anomalies in the northern and southern hemispheres, and the differences between the two in order to estimate the time trend coefficients and the degrees of persistence in the data. Analysing the entire sample period (from 1880m1 to 2022m10), the results indicate lower degrees of persistence and more acute trends in northern hemisphere temperatures. We also observe some degree of persistence and a positive trend for the differenced series. Finally, observing the evolution of these two factors over the last 50 years, we see that in northern temperatures, both the degree of persistence and the time trend have increased over time, whereas similar increases were not observed in southern temperatures.
PubDate: Wed, 21 Aug 2024 09:35:55 +000
- The Gust Factor Models Involving Wind Speed and Temperature Profiles for
Wind Gust Estimation
Abstract: This study analyzed the deviation of the gust factor (GF) method in gust estimation and the differences in the vertical distribution of upper-level wind speed and temperature when varying deviations occurred using observed data from 11 stations in central-eastern China from January 2017 to December 2020. A unified upper-level gust impact model was developed through multiple regression (GF-L) and machine learning (GF-M) methods based on data from these stations to improve gust estimation accuracy. The effectiveness of the GF, GF-L, and GF-M methods was compared using data from January 2021 to October 2022. Results showed that integrating the upper-level gust impact model into the GF method reduced underestimation and improved accuracy, with the GF-M method showing the most significant improvement. The GF-L method reduced the root mean square error by 5.1% on average compared to the GF method, while the GF-M method achieved a 9.2% reduction. The study confirmed the applicability of the unified upper-level gust impact model across different stations, with the GF-M method demonstrating better results.
PubDate: Wed, 14 Aug 2024 11:04:17 +000
- Assessing the Impact of Climate Change on Agricultural Water Management in
Mainland Southeast Asia
Abstract: This study aims to address an assessment of climate change’s impact on agricultural water management in mainland Southeast Asia (MSEA). We used several agroclimatic indices, such as consecutive dry days (CDDs), maximum number of consecutive wet days (CWDs), consecutive summer days (CSUs), cold spell duration index (CDSIs), and warm and wet days (WWs), based on the Geophysical Fluid Dynamics Laboratory Earth System Model to characterize the effect of climate on crop water need (CWN) in MSEA. The climate model shows monthly precipitation and temperature patterns with acceptable accuracy but with an underestimation of precipitation and a warm bias in temperature. CDDs show a significant increase in aridity and drought occurrences, particularly in northern Myanmar, Laos, Vietnam, and northern Thailand, across different representative concentration pathways. CSUs have been seen to have a substantial influence on the region’s agricultural economy. The CDSIs, on the other hand, show a decrease in the duration of cold spells, indicating the existence of milder climatic conditions that could potentially affect crop growth. The CWDs show a decreasing trend in most of the multiple regions in Thailand, Laos, Vietnam, Cambodia, and Malaysia. Though the WW index shows more wet days, this does not immediately imply improved crop growth; rather, it highlights possible changes in water availability that could affect agricultural practices. While negative CWNs in the dry season months like March and November suggest possible water shortages, which pose risks to agriculture and food security in Myanmar, northern and eastern Thailand, and Cambodia, especially at the end of the century, increases in CWNs during the rainy season correspond with anticipated higher water demands for agriculture.
PubDate: Thu, 25 Jul 2024 15:49:26 +000
- Mesoscale Convective Systems in Central Africa: Characteristics of the
Associated Seasonal and Diurnal Cycle of Observed Surface Meteorological
Parameters
Abstract: This study examines mesoscale convective systems (MCSs) in relation to 1-min automatic weather station data for 2 years over the city of Yaounde. The focus is on characterising the atmospheric variability associated with MCS activity while distinguishing the days with and without MCS activities. This paper aims to determine the diurnal cycles of occurrence frequencies and percentages of rainfall, relative humidity, dew point temperature, solar radiation, temperature, and wind speed for days with and without MCSs. There are more than 623 MCS events during the study period (over 150 events per rainy season). The link between MCS activity and regional-scale circulation and atmospheric instability is investigated. The diurnal cycle of the number of MCSs shows a maximum in the afternoon (around 1,600–2,200 LT), a morning minimum (around 0700–1,300 LT), and substantial activity during the night. Surface relative humidity is 5% lower on non-MCS days, surface dew point 2% higher on MCS days between 0700 and 1800 hr, and solar radiation higher on MCS days between 0500 and 1000 hr. The percentage of rainfall associated with MCSs can exceed 60% on an annual scale and up to 80% on a seasonal scale. MCS activity is associated with instability in the lower troposphere, and this convective instability is maximal during the peak of the MCS activity.
PubDate: Thu, 11 Jul 2024 13:48:49 +000
- Effects of Latent Heat Release from Single-Moment and Double-Moment
Microphysical Schemes on the Simulated Intensity of Typhoon Songda
Abstract: This study aims to quantify the magnitude of latent heat release by strong typhoons in the Northwest Pacific region and to identify the key cloud microphysical processes that affect the release of latent heat. The Weather Research and Forecasting mesoscale numerical model was used to simulate Typhoon Songda from 2011. Single- and double-moment 6-class (WSM6 and WDM6) cloud microphysics schemes were used to simulate the 3D structure and evolution of latent heat release. Simulations show that condensation of cloud water is the main source of latent heat release, while the main sources of latent heat absorption are evaporation of rain and cloud water. Depositional heating in cold cloud processes in the upper troposphere also plays an important role in the evolution of typhoon intensity. Latent heat release and absorption simulated by both schemes evolve through the phases of intensifying, maintaining, and weakening during the lifetime of the typhoon. The largest latent heat release occurs at altitudes between 5 and 10 km; condensational heating plays a major role at altitudes below 6 km, while depositional heating is the dominant process at altitudes from 6 to 12 km. Compared with WSM6, the WDM6 scheme produces smaller absolute values of latent heating from condensation and evaporation of cloud water and a more reasonable vertical distribution of cloud water mixing ratio. Positive latent heating from WDM6 is larger than that from WSM6. However, because of stronger processes of evaporation of rainwater and sublimation of cloud ice, total net latent heat release is lower in WDM6, and hence, simulated typhoon intensity is higher in WSM6.
PubDate: Thu, 20 Jun 2024 11:34:06 +000
- Assessment of Traffic-Related Ambient Air Particulate Matter (PM) Levels
in Douala, Cameroon
Abstract: A challenge in many Sub-Saharan African (SSA) cities is the absence of air quality monitoring due to the high expense and technical expertise needed. Air pollution around the world is becoming a societal problem, and developed countries have all the necessary information about the levels of most air pollutants; however, underdeveloped countries have inadequate information on air quality. The problem of poor air quality in many SSA countries is due to lack of information about the hazards of poor air quality, lack of monitoring equipment, and lack of measuring stations: this is the case in Cameroon. We are avoiding these problems by using portable, low-cost, low-maintenance air quality monitoring equipment, including the OC 300 dust particle laser, to observe pollution levels of particle matter levels in the PK17 Douala-Cameroon, a locality in the Central African region where no air quality data previously existed. The air quality index is very poor on the measurement days, and the color code is red. It has maximum values of about 199 for PM10 and 192 for PM2.5. The average concentration values obtained were of the order of 200.54 µg·m−3, 155.54 µg·m−3, 194.90 µg·m−3, and 194.03 µg·m−3 for PM10, respectively, on Tuesday, Thursday, Saturday, and Sunday. Regarding PM2.5, the average concentrations are 99.84 µg·m−3, 82.30 µg·m−3, 108.23 µg·m−3, and 112.97 µg·m−3, respectively, on Tuesday, Thursday, Saturday, and Sunday. A two-dimensional Gaussian model was used to estimate the particle matter concentration along each road. The nRMSE was 9% for PM10 and 12 for PM2.5, and the index-d for both particle sizes was close to 1 (0.74 for PM10 and 0.98 for PM2.5), which allows us to conclude that the calculated concentrations are the closest to the measured concentrations. The assessment reveals that most of the particulate matter during the measurement period was generated by road traffic. The concentration measured values of the pollutants are three times for PM2.5 and four times for PM10 above the recent WHO limits for daily exposure. In addition, it provides essential information on the pollution in Douala, which can be a major source of illness in the city and must be addressed.
PubDate: Thu, 06 Jun 2024 06:18:27 +000
- Nonstationary Changes in Annual Rainfall over Indonesia’s Maritime
Continent
Abstract: The investigation into changing rainfall patterns in the Indonesian Maritime Continent (IMC) involved testing for trends, step changes, and variance nonstationarity using the Mann–Kendall, Pettitt, and White tests, respectively. The analysis covered data from 106 meteorological stations over the period from 1981 to 2021, with all tests conducted at annual time scales to understand the evolving precipitation dynamics in the region. According to the findings of this study, the average annual rainfall in IMC is nonstationary. Rainfall has increased dramatically by 12.72 mm with a significant shift point in 1994. However, this characteristic is likely to vary if the analysis period is extended, reduced, or conducted in different time frames. The spatial analysis indicates that 35.8% of meteorological stations observed a notable increase in rainfall, while 28.3% experienced a significant shift, and 16% displayed considerable variation. Consequently, the study only partially identified the nonstationary nature of rainfall in the IMC. Moreover, the research highlights a substantial rise in rainfall in the central to eastern IMC region, whereas the central to western region predominantly shows a decrease. This nonstationary test helps avoid errors in climatic conditions and analytical methods, presenting recommendations for hydrological projects. Consequently, employing this nonstationary test helps prevent inaccuracies in understanding climatic conditions and analytical techniques, thereby offering valuable suggestions for hydrological projects.
PubDate: Wed, 22 May 2024 13:50:00 +000
- Modelling Land Surface Temperature Variation in New Guinea Island from
2000 to 2019 Using a Cubic Spline Model
Abstract: Land surface temperature (LST) is a critical indicator variable in climate science. In this study, the variation of LST on the island of New Guinea during 2000 to 2019 was investigated using a cubic spline model and a multivariate regression model. The data were obtained from the National Aeronautics and Space Administration moderate resolution imaging spectroradiometer database. This study focused on 90 subregions with 105-pixels of latitude 90 kilometer apart. These subregions were categorized into 10 super-regions. The results showed that the mean change in LST for all 90 subregions was +0.086°C per decade with a confidence interval of (0.028, 0.144)oC. There were five super-regions with a significant mean LST change. LST increased significantly in the central-north, central-south of the island (super-regions B1, C1, and C2 with 0.117°C, 0.162°C, and 0.185°C, respectively) and the southern part of Papua New Guinea (super-region E2 with 0.217°C), whereas it decreased in the middle part of the Indonesian territories (A2 with −0.122°C). The results also showed that LST variation occurs at the subregional level. Climate change mitigation methods are critical for reducing temperature rise and limiting any negative effects on the region.
PubDate: Mon, 20 May 2024 14:05:00 +000
- Sensitivity of WRF-Simulated 2 m Temperature and Precipitation to
Physics Options over the Loess Plateau
Abstract: The current paper evaluates the weather research and forecasting (WRF) model sensitivity to five different combinations of cumulus, microphysics, radiation, and planetary boundary layer (PBL) schemes over Loess Plateau for the period 2015, in terms of 2 m temperature and precipitation. The WRF configuration consists of a 10 km resolution domain nested in a coarser domain driven by European Center for Medium-Range Weather Forecasts Reanalysis (ERA-Interim) data. The model simulated 2 m temperature and precipitation have been evaluated at daily and monthly scales with gridded observational dataset. The analysis shows that all experiments reproduce well the daily 2 m temperature, with overestimation particularly in the low-temperature range. Precipitation is less well simulated, with underestimation in all range, especially for intense rainfall. Comparing with ERA-Interim, WRF shows no clear benefit in simulating daily 2 m temperature while prominent improvement in simulating daily precipitation. WRF simulations capture the annual cycle of monthly 2 m temperature and precipitation with a warm bias and wet bias for most experiments in summer. Some reasonable configurations are identified. The “best” configuration depends on the criteria.
PubDate: Thu, 02 May 2024 08:50:01 +000
- Analysis of Urban Heat Island Effect in Wuhan Urban Area Based on
Prediction of Urban Underlying Surface Coverage Type Change
Abstract: The rapid development of urbanization makes the phenomenon of urban heat islands even more serious. Predicting the impact of land cover change on urban heat island has become one of the research hotspots. Taking Wuhan, China, as an example, this study simulated the land type change in 2020 through the Cellular Automata-Markov-Chain (CA-Markov) model. The urban heat island in 2020 was simulated and analyzed in conjunction with the Weather Research & Forecasting Model (WRF), and the simulation results of wind velocity and temperature were confirmed using weather station observation data. Based on this, the land cover and urban heat island of Wuhan in 2030 were predicted. The temperature was found to be well-fit by CA-Markov simulated land use data, with an average inaccuracy of about 2.5°C for weather stations. Wind speed had a poor fitting effect; the average error was roughly 2 m/s. The built-up area was the center of the high temperature area both before and after the prediction, the water was the low temperature area, and the peak heat island happened at night. According to the forecast results, there will be more built-up land in 2030, and there will be a greater intensity of heat islands than in 2020.
PubDate: Mon, 22 Apr 2024 14:50:01 +000
- Temporal Dynamics and Trend Analysis of Areal Rainfall in Muger
Subwatershed, Upper Blue Nile, Ethiopia
Abstract: This study was employed to investigate the temporal variability and trend analysis of areal rainfall in the Muger subwatershed, Upper Blue Nile, Ethiopia. The study was run over the following procedures to handle the main objective: (1) determining the areal rainfall from gauged point rainfall using the Thiessen polygon method, (2) grouping the months in the season according to the study area, (3) evaluating the temporal dynamics of annual and seasonal areal rainfall using the coefficient of variation (CV), standard anomaly index (SAI), and precipitation concentration index (PCI), and (4) analyzing the trend of annual and seasonal areal rainfall using modified Mann–Kendall’s (modifiedmk) test in RStudio. Based on the temporal variability analysis, CV results depict that annual and summer areal rainfall had low variability with values of 13.43% and 13.7%, respectively. Winter and spring areal rainfall shows high variation with a CV value of 50.5% and 36%, respectively. According to the SAI output, around 70% of the considered year was in the normal condition of wetness. On the other hand, the seasonal (winter, spring, and summer) rainfall distribution of the study area shows strong irregularity distribution throughout the considered years as a result of PCI with a value of 57.5%. The trend of the areal rainfall was shown to be both increasing and decreasing. However, the trend was insignificant with a 10% confidence level.
PubDate: Tue, 16 Apr 2024 07:20:01 +000
- Statistical Analysis for the Detection of Change Points and the Evaluation
of Monthly Mean Temperature Trends of the Moulouya Basin (Morocco)
Abstract: This study examines the spatiotemporal variability of mean monthly temperature in the Moulouya watershed of northeastern Morocco, highlighting associated trends. To this end, statistical methods widely recommended by climate researchers were adopted. We used monthly mean temperature data for the period 1980–2020 from 9 measuring stations belonging to the Moulouya Watershed Agency (ABHM). These stations were rigorously selected, taking into account their reliability, the length of their records, and their geographical position in the basin. In addition, a quality test and homogenization of the temperature series were carried out using the Climatol tool. The results obtained show a significant upward trend in mean monthly temperature, mainly pronounced during the summer months, in the Moulouya watershed. In fact, Z values generally exceeded the 0.05 significance level at all stations during April, May, June, July, August, and October. According to the results of Sen’s slope test, mean monthly temperatures show an annual increase ranging from 0 to 0.13°C. The maximum magnitude of warming is recorded in July, specifically at Oujda Station. On an overall watershed scale, May, August, and July show a rapid warming trend, with average rates of 0.093, 0.086, and 0.08°C per year, respectively. By contrast, the series for the other months show no significant trend. Significant trend change points were also identified at watershed and station scales, mainly around 2000, primarily for accelerated warming of the summer months.
PubDate: Wed, 10 Apr 2024 09:50:01 +000
- Ultraviolet Radiation Quasi-Periodicities and Their Possible Link with the
Cosmic Ray and Solar Interplanetary Data
Abstract: In this study, solar ultraviolet (UV) radiation data collected in Riyadh, Saudi Arabia, between 2015 and 2022 were analyzed to explore quasi-periodicities in the UV time series. The power spectrum density analysis revealed several local peaks that exceeded the 95% confidence interval. These peaks included periodicities of 483–490 days, 272 days, 157−162 days, 103−110 days, 64–72 days, 27 days, and 13 days. To investigate the potential influence of space weather parameters on UV radiation, data on cosmic rays, solar radio flux at 10.7 cm (F10.7 cm), the Kp index, and solar wind speed for the same time period were examined. The aim was to identify periodicities in these variables that aligned with those found in the UV radiation data. The analysis reveals that several periodicities observed in the UV radiation spectrum are also present in the spectra of the considered parameters. Prominent periodicities include a 270-day cycle in UV radiation and cosmic rays, as well as periodicities of 72 days, 27 days, and 13 days in all considered variables. Furthermore, 110-day peaks are observed in spectrum of the UV radiation, the Kp index, solar radio flux F10.7, and solar wind speed. Notably, consistent peaks at 157-day periodicity are identified in the UV spectrum, also present in the spectra of all the considered variables (cosmic rays ∼162 days, Kp index ∼162 days, solar radio flux ∼156 days, and solar wind speed ∼163 days). The identification of common periodicities between UV radiation and space weather parameters in this study provides compelling evidence of a potential direct or indirect influence of solar variations on UV radiation. This finding significantly enhances our understanding of the impact of extraterrestrial factors, particularly solar activity, on the Earth’s environment.
PubDate: Tue, 19 Mar 2024 11:35:01 +000
- Power Data Access Viewer-Based Meteorological Drought Analysis and
Rainfall Variability in the Nile River Basin
Abstract: Meteorological drought poses a frequent challenge in the Nile River basin, yet its comprehensive evaluation across the basin has been hindered by insufficient recorded rainfall data. Common indices like the standard precipitation index, coefficients of variation, and precipitation concentration index serve as pivotal tools in gauging drought severity. This research aimed to assess the meteorological drought status in the Nile River basin by using the Power Data Access Viewer product rainfall data. Bias correction procedures were implemented to refine the monthly rainfall data for Bahirdar, Markos, Nekemt, and Muger stations, resulting in notable improvements in the coefficient of determination () that were increased from 0.74 to 0.93, 0.72 to 0.89, 0.71 to 0.96, and 0.69 to 0.84, respectively. The average spatial distribution of drought in the Nile basin was classified as extremely wet (3.81%), severely wet (9.01%), moderately wet (7.36%), near normal (9.97%), moderately drought (21.20%), severely drought (17.11%), and extremely drought (31.54%). Approximately 10.33% of the Nile River basin was situated in regions characterized by high rainfall variability, while around 21.17% was located in areas with a notably irregular precipitation concentration index. Overall, this study sheds light on the prevailing meteorological drought patterns in the Nile River basin, emphasizing the significance of understanding and managing these phenomena for the sustainable development of the region.
PubDate: Tue, 05 Mar 2024 08:35:01 +000
- False Alarm Causes and Wind Field Sensitivity Analysis of a Severe
Rainfall Event in the Guangdong-Hong Kong-Macao Greater Bay Area Urban
Cluster
Abstract: On May 11, 2022, despite the favorable upper and lower-level circulation patterns of the high-altitude trough, shear line, and southwest jet stream, the urban cluster of the Guangdong-Hong Kong-Macao Greater Bay Area experienced light to moderate rainfall, deviating significantly from the forecasted heavy rain and local heavy rainstorm. This study explores the reasons for false alarms and predictability using ground observation data, radar data, ECMWF-ERA5 reanalysis field data, and ECMWF and CMA-TRAMS forecast data. The results indicate that the warm and moist airflow transported by the low-level jet stream was intercepted by the upstream MCS (mesoscale convective system) along the coastal area of western Guangdong, and inadequate conditions of negative vorticity dynamics led to insufficient moisture, thermodynamic, and dynamic conditions over the urban cluster, preventing the triggering of heavy precipitation. In addition, the 700 hPa westerly flow guiding the airflow and the stable low-level shear line, coupled with surface convergence lines, influenced the northward or southward movement of MCSs along the coastal and inland regions of western Guangdong. The weak and discontinuous intensity of echoes in the upstream Zhaoqing region further hindered the influence of surrounding echoes on the urban cluster. Numerical forecast models ECMWF and CMA-TRAMS overestimated the 850 hPa windspeed and 925 hPa meridional windspeed, resulting in the forecasted urban cluster experiencing heavy rain. Sensitivity tests of wind fields indicate that the 850 hPa wind field information is more sensitive to precipitation in the urban cluster. In this process, weak signal correction can be achieved in strong precipitation forecasts using the distinct signal of lower 850 hPa water vapor flux divergence compared to 925 hPa. Therefore, in the future, when the Guangdong-Hong Kong-Macao Greater Bay Area encounters similar warm-sector heavy rainfall events, adjustments to model forecasts can be made using specific 850 hPa elements such as wind speed, water vapor flux divergence, or specific humidity to enhance predictive accuracy.
PubDate: Mon, 04 Mar 2024 11:05:01 +000
- Application of wetPf2 Data for Investigating Characteristics of
Temperature and Humidity of Air Masses over Paracel and Spratly Islands
Abstract: This article uses data from the second-generation Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC-2) satellites (wetPf2) to study the temperature and humidity properties of the air masses over Paracel and Spratly Islands in the Vietnam East Sea (South China Sea). The satellite observational data were validated with the radiosonde data from three stations in Vietnam: Hanoi, Danang, and Ho Chi Minh City. Subsequently, the wetPf2 data are used to analyze the characteristics of temperature and relative humidity variations of the air masses over the Paracel and Spratly regions. Results show that the mean error of the satellite observational data for temperature ranges from −0.06°C to −0.02°C, with standard deviations ranging from 0.73°C to 1.04°C. The mean error of relative humidity fluctuates between 11.6% and 12.5%, with standard deviations ranging from 15.1% to 19.1%. The values are reasonable and comparable to those in previous studies. Seasonal variations of temperature and humidity show that the air mass over the Paracel Islands exhibits a larger annual temperature with an annual variation of approximately 5.0°C, significantly higher than the value of 2.2°C in the air mass over the Spratly Islands. The difference may be due to the greater influence of continental and seasonal wind systems in the northern region. Within both air masses, the annual temperature variation in the boundary layer is much larger than that in the free atmosphere. Annual relative humidity variation is higher in summer and autumn than in winter and spring. The significant changes in the relative humidity with height during winter and no significant change of the relative humidity with height during summer may be related to the important role of strong convective activity carrying moist air upward to higher atmospheric levels during the summer time.
PubDate: Wed, 28 Feb 2024 09:05:01 +000
- Information Entropy-Based Hybrid Models Improve the Accuracy of Reference
Evapotranspiration Forecast
Abstract: Accurate forecasting of reference crop evapotranspiration (ET0) is vital for sustainable water resource management. In this study, four popularly used single models were selected to forecast ET0 values, including support vector regression, Bayesian linear regression, ridge regression, and lasso regression models, respectively. They all had advantages of low requirement of data input and good capability of data fitting. However, forecast errors inevitably existed in those forecasting models due to data noise or overfitting. In order to improve the forecast accuracy of models, hybrid models were proposed to integrate the advantages of the single models. Before the construction of hybrid models, each single model’s weight was determined based on two weight determination methods, namely, the variance reciprocal and information entropy weighting methods. To validate the accuracy of the proposed hybrid models, 1–30 d forecast data from January 2 to February 1, 2022, were used as a test set in Xinxiang, North China Plain. The results confirmed the feasibility of the information entropy-based hybrid model. In detail, the information entropy model generated the mean absolute percentage errors of 11.9% or a decrease by 48.9% compared to the single and variance reciprocal hybrid models. Moreover, the model generated a correlation coefficient of 0.90 for 1–30 d ET0 forecasting or an increase by 13.6% compared to other models. The standard deviation and the root mean square error of the information entropy model were 1.65 mm·d−1 and 0.61 mm·d−1 or had a decrease by 16.4% and 23.7%. The maximum precision and the F1 score were 0.9618 and 0.9742 for the information entropy model. It was concluded that the information entropy-based hybrid model had the best midterm (1–30 d) ET0 forecasting performance in the North China Plain.
PubDate: Sat, 03 Feb 2024 04:35:01 +000
- Frequentist and Bayesian Approaches in Modeling and Prediction of Extreme
Rainfall Series: A Case Study from Southern Highlands Region of Tanzania
Abstract: This study focuses on modeling and predicting extreme rainfall based on data from the Southern Highlands region, the critical for rain-fed agriculture in Tanzania. Analyzing 31 years of annual maximum rainfall data spanning from 1990 to 2020, the Generalized Extreme Value (GEV) model proved to be the best for modeling extreme rainfall in all stations. Three estimation methods–L-moments, maximum likelihood estimation (MLE), and Bayesian Markov chain Monte Carlo (MCMC)–were employed to estimate GEV parameters and future return levels. The Bayesian MCMC approach demonstrated superior performance by incorporating noninformative priors to ensure that the prior information had minimal influence on the analysis, allowing the observed data to play a dominant role in shaping the posterior distribution. Furthermore, return levels for various future periods were estimated, providing guidance for flood protection measures and infrastructure design. Trend analysis using value, Kendall’s tau, and Sen’s slope indicated no statistically significant trends in rainfall patterns, although a weak positive trend in extreme rainfall events was observed, suggesting a gradual and modest increase over time. Overall, the study contributes valuable insights into extreme rainfall patterns and underscores the importance of L-moments in identifying the best fit distribution and Bayesian MCMC methodology for accurate parameter estimation and prediction, enabling effective measures and infrastructure planning in the region.
PubDate: Tue, 30 Jan 2024 11:35:01 +000
- Diurnal Variation Characteristics of Raindrop Size Distribution Observed
by a Parsivel2 Disdrometer in the Ili River Valley
Abstract: The diurnal variation characteristics of raindrop size distribution (RSD) in the Ili River Valley are investigated in this study, using the RSD data from May to September during 2020-2021 collected by a Parsivel2 disdrometer in Zhaosu. Significant diurnal variations (02–07, 08–13, 14–19, and 20-01 local standard time (LST)) of precipitation and RSD in Zhaosu are revealed during the rainy seasons. Precipitation mainly occurs in the late afternoon and early evening. A higher concentration of small raindrops is observed in the morning, whereas more mid-size and large raindrops are observed in the afternoon. The RSD exhibits diurnal differences between different rainfall rate classes; the diurnal difference of RSD is more pronounced in the case of high rainfall rates. Stratiform precipitation can occur at any time of the day, yet convective precipitation mainly occurs during the late afternoon and early evening. The RSD of stratiform rainfall shows a similar distribution over the four time periods. For convective rainfall, the concentration of small raindrops is the highest (lowest) over 02–07 (14–19) LST, while the highest (lowest) concentration of medium and large drops is observed over 14–19 (02–07) LST. Convective rain in the Ili River Valley over 14–19 LST can be characterized as the continental convective cluster, while in the rest time of the day, it is neither in the maritime cluster nor in the continental cluster. The empirical relationships between the radar reflectivity factor and rainfall rate (Z-R) for stratiform and convective rain types are also derived. The purpose of this study is to advance our understanding of precipitation microphysics in arid mountainous region.
PubDate: Tue, 16 Jan 2024 09:20:02 +000
- Identifying the Moisture Sources in Different Seasons for Abaya-Chamo
Basin of Southern Ethiopia Using Lagrangian Particle Dispersion Model
Abstract: Understanding the sources of precipitation and their impacts is crucial for basin-wide water balance research. Previous research concentrated on the sources of moisture in Ethiopia. The southern part’s moisture sources, however, were not investigated. The primary objective of this study is to trace the source of atmospheric moisture in the Abaya-Chamo sub-basin of southern Ethiopia using numerical water vapor tracers like Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Exploring the possible regions of atmospheric vapor roots and the path of moist air initiating rainfall that reaches the basin was feasible for the year 2018–2020. The anticyclone from the Arabian High, which is positioned in the Arabian and Mediterranean seas, was the primary source of moisture supply in the study area during the Belg (March to May) season, according to the back trajectory cluster analysis results. Additionally, the Indian Ocean adds moisture resulting from Mascarene highs brought by equatorial easterlies. Furthermore, during Kiremt (June to September), air masses from the Congo basin were the potential moisture source region for the study areas in combination with air masses originating from the Mascarene highs, located in the South Indian Ocean, and the St. Helena high, centered in the subtropical southern Atlantic Ocean. This study primarily focuses on the complex dynamics of atmospheric moisture sources around Abaya-Chamo sub-basin of southern Ethiopia, offering insight into seasonal fluctuations and contributing various components. These findings contribute to basin-specific water balance research by filling gaps in the previous studies.
PubDate: Tue, 09 Jan 2024 09:05:00 +000
- Air Temperature Modeling Based on Land Surface Factors by the Cubist
Method (Case Study of Hamoun International Wetland)
Abstract: The drying up of Hamoun International Wetland (HIW) and the loss of vegetation in this area have led to an increase in ambient temperature. This research examines the changes in the surface of HIW and its role in air temperature (Tair) using data on land surface temperature (LST), vegetation, wind speed, and relative humidity. The Cubist regression model (CRM) is used to simulate the effects of land surface factors (LSFs) on Tair. Four microsites with different plant cover percentages were selected for this purpose. After data collection, 75% of the data were used for modeling and 25% of the data were used for model testing. The results showed that CRM has adequate performance for estimating Tair. The assessment of the relationship between land surface temperature (LST) and Tair at 2 meter height showed that there was a high correlation coefficient between 0.86 and 0.91 in the different microsites. The results of using CRM for estimating Tair showed that this model can estimate air temperature from independent parameters of LST, wind speed, vegetation percentage, and relative humidity with a correlation coefficient of 0.98. In this model, the LST, relative humidity, and vegetation percentage were entered with values of 100%, 93%, and 83% respectively. Wind speed was not included in the model because the measurements were constant and less than 4 m/s throughout the period (no changes).
PubDate: Mon, 08 Jan 2024 09:50:02 +000
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