 Subjects -> METEOROLOGY (Total: 106 journals)
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- Evaluation of Satellite Precipitation Products for Estimation of Floods in
Data-Scarce Environment
Abstract: Utilization of satellite precipitation products (SPPs) for reliable flood modeling has become a necessity due to the scarcity of conventional gauging systems. Three high-resolution SPPs, i.e., Integrated Multi-satellite Retrieval for GPM (IMERG), Global Satellite Mapping of Precipitation (GSMaP), and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), data were assessed statistically and hydrologically in the sparsely gauged Chenab River basin of Pakistan. The consistency of rain gauge data was assessed by the double mass curve (DMC). The statistical metrics applied were probability of detection (POD), critical success index (CSI), false alarm ratio (FAR), correlation coefficient (CC), root mean square error (RMSE), and bias (B). The hydrologic evaluation was conducted with calibration and validation scenarios for the monsoon flooding season using the Integrated Flood Analysis System (IFAS) and flow duration curve (FDC). Sensitivity analysis was conducted using ±20% calibrating parameters. The rain gauge data have been found to be consistent with the higher coefficient of determination (R2). The mean skill scores of GSMaP were superior to those of CHIRPS and IMERG. More bias was observed during the monsoon than during western disturbances. The most sensitive parameter was the base flow coefficient (AGD), with a high mean absolute sensitivity index value. During model calibration, good values of performance indicators, i.e., R2, Nash−Sutcliffe efficiency (NSE), and percentage bias (PBIAS), were found for the used SPPs. For validation, GSMaP performed better with comparatively higher values of R2 and NSE and a lower value of PBIAS. The FDC exhibited SPPs’ excellent performance during 20% to 40% exceedance time.
PubDate: Wed, 03 May 2023 15:05:00 +000
- Accuracy Evaluation of Standardized Precipitation Index (SPI) Estimation
under Conventional Assumption in Yeşilırmak, Kızılırmak, and Konya
Closed Basins, Turkey
Abstract: The doubt in the calculation algorithm of the standardized precipitation index (SPI), which is widely preferred in the evaluation and monitoring of drought, still remains up-to-date because its calculation process is performed in the form of standardization or normalization with a default probability distribution. Therefore, the success of this index is directly affected by the choice of the probability distribution model. This study is based on the effect of three different parameter estimation methods on the calculation process, as well as the comparison of the SPI results calculated based on the default Gamma distribution and the distribution with the best ability to represent the 3-and 12-month consecutive summed rainfall data among the 15 candidate distributions namely Gamma (GAM), Generalized Extreme Value (GEV), Pearson Type III (P III), Log Pearson Type III (LP III), two-parameter Lognormal (LN2), three-parameter Lognormal (LN3), Generalized Logistic (GLOG), Extreme Value Type I (EVI), Generalized Pareto (GPAR), Weilbul (W), Normal (N), Exponential (EXP), Logistic (LOG), four-parameter Wakeby (WK4), and five-parameter Wakeby (WK5) distributions. Approximately 68.4% and 18.4% of the 3-month data considered had the best fit to the Weibull and Pearson III distribution, while approximately 24% and 18% of the 12-month data had the best fit to the Weibull and Logistic distribution. On the other hand, it was found that the default Gamma distribution calculated the extreme drought categories significantly more than the best-fit distribution model. In terms of parameter estimation methods, L-moments for 3-month series and maximum likelihood approaches for 12-month series were most dominant.
PubDate: Mon, 17 Apr 2023 11:05:01 +000
- Orographic Effect and the Opposite Trend of Rainfall in Central Vietnam
Abstract: Central Vietnam is characterized by severe flooding associated with heavy rainfall events caused by interactions between multiscale atmospheric circulations and the complex local terrain. Previous studies believed rainfall in central Vietnam is closely related to the cold surge; however, it fails to explain the cause of the early rainfall occurrence in August in the subregion. For the first time, this study investigates the detailed atmospheric mechanisms associated with rainfall variations in central Vietnam using the empirical orthogonal function (EOF) applied to the recently developed high-resolution Vietnam gridded precipitation (VnGP) dataset. Reanalysis data NCEP/NCAR is used to associate the rainfall changes with respective atmospheric mechanisms. EOF analysis detected two dominant rainfall modes. The primary mode explains the rainfall variation from October to November over the central and is directly related to the interaction of cold surges and tropical disturbances. The second mode accounts for rainfall occurring in north central from September to mid-October, which is attributed to the westerly summer monsoon activities. Also, we revealed that, while the first mode exhibits a significant correlation with El Niño-southern oscillation, the second depends highly on the contrast of sea surface temperature in the northern and southern Hemispheres. This different oceanic forcing and the local topological effect of Truong Son mountain range reasonably explain the opposite rainfall pattern in central Vietnam in early fall.
PubDate: Sat, 11 Mar 2023 06:35:01 +000
- Spatiotemporal Variability of Extreme Rainfall in Southern Benin in the
Context of Global Warming
Abstract: Changes in the frequency and timing of extreme precipitation in southern Benin are assessed in the context of global warming. The peak-over-threshold (POT) is used for this purpose, with the six (06) year return period daily rainfall as the threshold over seventeen (17) weather stations between 1960 and 2018. The results show that the South Benin experienced extreme rainfall on many occasions between 1960 and 2018 with a nonuniform spatiotemporal distribution of this category of rainfall. No statistically significant trend in the frequency and variation of extreme rainfall intensities is revealed over the study period. Despite the low rate of extreme rainfall, the monthly trend is consistent with the bimodal rainfall regime in southern Benin. The global warming highlighted in its last decades in southern Benin is accompanied by a slightly upward trend in extreme rainfall compared to the period before 1990.
PubDate: Wed, 08 Mar 2023 14:35:01 +000
- Study on the Impact of Future Climate Change on Extreme Meteorological and
Hydrological Elements in the Upper Reaches of the Minjiang River
Abstract: Global warming increases global average precipitation and evaporation, causing extreme climate and hydrological events to occur frequently. Future changes in temperature, precipitation, and runoff from 2021 to 2050 in the upper reaches of the Minjiang River were analyzed using a distributed hydrological model, the SWAT (Soil and Water Assessment Tool), under a future climate scenario. Simultaneously, future variation characteristics of extreme climate hydrological elements in the upper reaches of the Minjiang River were analyzed using extreme climate and runoff indicators. The research shows that the frequency and intensity of the extreme temperature warming index will increase, while those of the extreme temperature cooling index will increase and then weaken in the upper reaches of the Minjiang River under a future climate scenario. The duration of precipitation, the intensity of continuous heavy precipitation, and the frequency of heavy precipitation will increase, whereas the intensity of short-term heavy precipitation and the frequency of heavy precipitation will decrease. However, spatial distribution of flood in the upper reaches is different, and thus flood risk in the upstream source area will still tend to increase. Particular attention should be given to the increase in autumn flood risk in the upper reaches of the Minjiang River.
PubDate: Thu, 09 Feb 2023 15:50:01 +000
- Climatology Definition of the Myanmar Southwest Monsoon (MSwM): Change
Point Index (CPI)
Abstract: Myanmar’s climate is heavily influenced by its geographic location and relief. Located between the Indian summer monsoon (ISM) and the East Asian summer monsoon (EASM), Myanmar’s climate is distinguished by the alternation of seasons known as the monsoon. The north-south direction of peaks and valleys creates a pattern of alternate zones of heavy and scanty precipitation during both the northeast and southwest monsoons. The majority of the rainfall has come from Myanmar’s southwest monsoon (MSwM), which is Myanmar’s rainy season (summer in global terms, June–September). This study explained both threshold-based and nonthreshold-based objective definitions of the onset and withdrawal of large-scale MSwM. The seasonal transitions in MSwM circulation and precipitation are convincingly represented by the new index, which is based on change point detection of the atmospheric moisture flow converging in the MSwM region (10–28 N, 92–102 E). A transition in vertically integrated moisture transport (VIMT), the reversal of surface winds, and an increase in precipitation may also be considered when defining MSwM onset objectively. We also define a change point of the MSwM (CPI) index for MSwM onset and withdrawal dates. The climatological mean onset of MSwM is day 135 (May 14), withdrawal is day 278 (October 4), and the total season length is 144 days. We are investigating spatial patterns of rainfall progression at and after the start of the monsoon, rather than transitions within a single region of the MSwM. The local southwest monsoon duration is well correlated with the CPI duration on interannual timescales, particularly in the peak rainfall regions, with a delay (advance) in large-scale onset or withdrawal associated with a delay (advance) of onset or withdrawal by local index. Hence, the next phase of this research is to study the maintenance and break of the monsoon to understand the underlying physical processes governing the monsoon circulation. The results of this study provide a possibility to reconstruct Myanmar’s monsoon climate dynamics, and the findings of this study can help unravel many remaining questions regarding the greater Asian monsoon system’s variability.
PubDate: Wed, 25 Jan 2023 12:05:00 +000
- Potential Impacts of Future Climate Changes on Crop Productivity of
Cereals and Legumes in Tamil Nadu, India: A Mid-Century Time Slice
Approach
Abstract: Climate change is a terrible global concern and one of the greatest future threats to societal development as a whole. The accelerating pace of climate change is becoming a major challenge for agricultural production and food security everywhere. The present study uses the midcentury climate derived from the ensemble of 29 general circulation models (GCMs) on a spatial grid to quantify the anticipated climate change impacts on rice, maize, black gram, and red gram productivity over Tamil Nadu state in India under RCP 4.5 and RCP 8.5 scenarios. The future climate projections show an unequivocal increase of annual maximum temperature varying from 0.9 to 2.2°C for RCP 4.5 and 1.4 to 2.7°C in RCP 8.5 scenario by midcentury, centered around 2055 compared to baseline (1981–2020). The projected rise in minimum temperature ranges from 1.0 to 2.2°C with RCP 4.5 and 1.8 to 2.7°C under RCP 8.5 scenario. Among the monsoons, the southwest monsoon (SWM) is expected to be warmer than the northeast monsoon (NEM). Annual rainfall is predicted to increase up to 20% under RCP 4.5 scenario in two-third of the area over Tamil Nadu. Similarly, RCP 8.5 scenario indicates the possibility of an increase in rainfall in the midcentury with higher magnitude than RCP 4.5. Both SWM and NEM seasons are expected to receive higher rainfall during midcentury under RCP 4.5 and RCP 8.5 than the baseline. In the midcentury, climate change is likely to pose a negative impact on the productivity of rice, maize, black gram, and red gram with both RCP 4.5 and RCP 8.5 scenarios in most places of Tamil Nadu. The magnitude of the decline in yield of all four crops would be more with RCP 8.5 over RCP 4.5 scenario in Tamil Nadu. Future climate projections made through multi-climate model ensemble could increase the plausibility of future climate change impact assessment on crop productivity. The adverse effects of climate change on cereal and legume crop productivity entail the potential adaptation options to ensure food security.
PubDate: Mon, 16 Jan 2023 02:50:01 +000
- Long-Term (2007 to 2018) Energy and CO2 Fluxes over an Agriculture
Ecosystem in the Southeastern Margin of the Tibetan Plateau
Abstract: Long-term eddy covariance flux observations over complex topography are crucial for improving the understanding of the turbulent exchanges between the land and atmosphere. Based on a 12-year (2007–2018) record dataset measured with the eddy covariance technique over the Dali agriculture ecosystem in the southeastern margin of the Tibetan Plateau, we investigated the diurnal, seasonal, and interannual variations of the sensible heat flux (Hs), latent heat flux (LE), and carbon dioxide flux (Fc), and their controlling variables. The results showed that Hs and LE exhibited similar diurnal and seasonal variations, while the amplitude of LE was clearly larger than that of Hs throughout the year. The turbulent fluxes showed remarkable fluctuation on the annual scale. The annual average Hs (LE) increases (decreases) from approximately 8 (110) W·m−2 during 2007–2013 to 20 (79) W·m−2 during 2014–2018. The annual cumulative net CO2 ecosystem exchange (NEE) increases significantly from approximately −739 g·C·m−2·yr−1 during 2007–2013 to −218 g·C·m−2·yr−1 during 2014–2018. The relationship between turbulent fluxes and meteorological variables was also examined. Wind speed (WS) is found to be the dominant controlling factor for the Hs on different temporal scales and their correlation coefficients increase when the timescales vary from daily to annual scale; whereas the product of WS and vapor pressure deficit (VPD) is the major meteorological variable controlling the LE over all temporal scales. The net radiation (Rn) is the dominating factor for Fc on daily and monthly timescales, while WS becomes the most controlling factor for Fc on an annual scale. Notably, surface energy and CO2 fluxes are also greatly influenced by the vegetation cover surrounding the measurement site.
PubDate: Fri, 23 Dec 2022 05:20:01 +000
- Influence of Underlying Surface on Distribution of Hourly Heavy Rainfall
over the Middle Yangtze River Valley
Abstract: The variation of boundary layer circulation caused by the influence of complex underlying surface is one of the reasons why it is difficult to forecast hourly heavy rainfall (HHR) in the middle Yangtze River Valley (YRV). Based on the statistics of high-resolution observation data, it is found that the low resolution data underestimate the frequency of HHR in the mountain that are between the twain-lake basins in the middle YRV (TLB-YRV). The HHR frequency of mountainous area in the TLB-YRV is much higher than that of Dongting Lake on its left and is equivalent to the HHR frequency of Poyang Lake on its right. The hourly reanalysis data of ERA5 were used to study the variation of boundary layer circulation when HHR occurred. It can be found that the boundary layer circulation corresponding to different underlying surfaces changed under the influence of the weather system. Firstly, the strengthening of the weather system in the early morning resulted in the strengthening of the southwest low-level air flow, which intensified the uplift of the windward slope air flow on the west and south slopes of the mountainous areas in the TLB-YRV. As a result, the sunrise HHR gradually increases from the foot of the mountain. The high-frequency HHR period of sunrise occurs when the supergeostrophic effect is weakened, the low-level vorticity and frontal forcing are strengthened, and the water vapor flux convergence begins to weaken. Secondly, the high-frequency HHR period of the sunset is caused by stronger local uplift and more unstable atmospheric stratification, but the enhanced local uplift is caused by the coupling of the terrain forcing of the underlying surface and the enhanced northern subgeostrophic flow, which causes the HHR to start closer to the mountain top at sunset than at sunrise.
PubDate: Wed, 21 Dec 2022 02:05:00 +000
- Seasonal Variability of Air Pollutants and Their Relationships to
Meteorological Parameters in an Urban Environment
Abstract: Air quality in urban areas is deteriorating over time with the increased pollutant distribution levels mainly caused due to anthropogenic activities. In addition, these pollutant distribution levels may relate to changing meteorological conditions. However, the relationships were not researched in-depth in the context of Sri Lanka, a country with a significant impact on climate change. The main objective of this study was to provide a broader perspective on the seasonal variation of tiny particles in air (PM2.5 and PM10), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), and sulfur dioxide (SO2) in two urban cities (Colombo and Kandy) in Sri Lanka over 3 years period (2018–2021) and the possible relationships between air pollution and meteorological variables. Results show that all the aforementioned pollutants except O3 consistently depict two peaks during the day, one in the morning (∼07:00–09:00 local time) and the other in the evening (∼18:00–20:00 local time). These peaks coincided with the traffic jams observed in both cities. The results further revealed that the concentration of all pollutants has significant seasonal variations. Compared to two monsoon seasons, the highest daily average PM2.5 (31.2 μg/m3), PM10 (49.5 μg/m3), NO2 (18.9 ppb), CO (717.5 ppb), O3 (18.5 ppb), and SO2 (9.4 ppb) concentrations in Colombo are recorded during northeast monsoon (NEM) seasons while contrast pattern is observed in Kandy. In addition, it was found that wind speed with its direction is the most influencing factor for the pollutant concentration except for SO2 and O3 in two cities, and this is irrespective of the season. This study’s findings contribute to understanding the seasonality of ambient air quality and the relationship between meteorological factors and air pollutants. These findings ultimately lead to designing and implementing season-specific control strategies to achieve air pollution reduction at a regional scale.
PubDate: Sat, 17 Dec 2022 03:05:00 +000
- The Relationship between the Atmospheric Heat Source over Tibetan Plateau
and the Westerly-Monsoon Evolution in August and Its Physical Mechanism
Abstract: In this study, the relationship between the East Asian subtropical westerly jet (EASWJ) and the East Asian summer monsoon (EASM) (westerly monsoon) and the correlation with the atmospheric heat source (AHS) on the Tibetan plateau (TP), especially the possible connection of the sudden enhancement of the correlation in August were analyzed. The results show that there is a significant correlation between the EASWJ and the EASM from June to October in terms of both intra-annual variability and interannual fluctuations, and the correlation between the AHS over TP and the EASWJ and the EASM during the same period is significantly enhanced in August. The synthetic analysis indicated that when the AHS was strong, a positive anomaly of a horizontal temperature gradient appeared over TP, which was conducive to the southward shift of the high-altitude temperature gradient center, resulting in the southward position of the axis of the 200 hPa westerly jet, and an upward and downward inclined westerly anomaly zone appeared from the south slope of TP to the main body and its north slope. Meanwhile, the East Asia–Pacific (EAP) teleconnection pattern with a negative phase appeared at 500 hPa, and TP to western Japan was located in the negative value area of the wave train. The AHS was conducive to the enhancement of the EAP negative phase, which was not conducive to the further northward transportation of water vapor by the EASM. On the contrary, when the AHS on TP was weak, the position of the westerly jet was northward and the EAP positive phase enhanced, contributing to the further northward transport of water vapor from the EASM.
PubDate: Wed, 14 Dec 2022 09:20:02 +000
- LiDAR-Based Windshear Detection via Statistical Features
Abstract: Windshear is a kind of microscale meteorological phenomenon which can cause danger to the landing and takeoff of aircrafts. Accurate windshear detection plays a crucial role in aviation safety. With the development of machine learning, several learning-based methods are proposed for windshear detection, i.e., windshear and non-windshear classification. To obtain accurate detection results, it is significant to extract features that can distinguish windshear and non-windshear properly from the obtained wind velocity data. In this paper, we mainly introduce two statistical indicators derived from the Doppler Light Detection and Ranging (LiDAR) observational wind velocity data by plan position illustrate (PPI) scans for windshear features construction. Besides the indicators directly derived from the wind velocity data, we also study the visual information from the corresponding conical images of wind velocity. Based on the proposed indicators, we construct three feature vectors for windshear and non-windshear classification. Inspired by the idea of multiple instance learning, the wind velocity data collected in the 4 minutes within the reported time spot are considered in the procedure of feature vector construction, which can reduce the possibility of windshear features missing. Both statistical methods and clustering methods are applied to evaluate the effectiveness of the proposed feature vectors. Numerical results show that the proposed feature vectors have good effect on windshear and non-windshear classification and can be used to provide more accurate windshear alerting to pilots in practice.
PubDate: Tue, 13 Dec 2022 11:20:01 +000
- Hydrological and Meteorological Drought Monitoring and Trend Analysis in
Abbay River Basin, Ethiopia
Abstract: The definition of drought is very controversial due to its multi-dimensional impact and slow propagation in onset and end. Predicting the accurate occurrence of drought remains a challenging task for researchers. The study focused on hydrological and meteorological drought monitoring and trend analysis in the Abbay river basin, using the streamflow drought index (SDI), standardized precipitation index (SPI), and reconnaissance drought index (RDI), respectively, to fill this research gap. The study also looked into the interrelationships between the two drought indicators. The SDI, SPI, and RDI were calculated using long-term streamflow, precipitation, and temperature data collected from 1973 to 2014. The data were collected from eight streamflow stations and fifteen meteorological gauge stations. DrinC software (Drought Indices Calculator) was used to calculate the SDI, SPI, and RDI values. The result from meteorological drought using SPI12 and RDI12 shows that 1975, 1981, 1984, 1986, 1991, 1994, and 2010 were extreme drought years, whereas 1983, 1984, 2001, and 2010 were the most extreme hydrological drought years based on the SDI12 result. Except for Bahir Dar and Gondar, a severe drought occurs at least once a decade in all stations considered in this study. In general, the SPI, RDI, and SDI results indicated that the study area was exposed to the most prolonged severe and extreme drought from 1981 to 1991. The findings of this study also demonstrated that the occurrence of hydrometeorological droughts in the Abbay river basin has a positive correlation at long time scales of 6 and 12 months. The trend analysis using the Mann–Kendall test implied that there was a significant meteorological drought trend in two stations (Debre Berhan and Fiche) at SPI12 and RDI12 time scale, but for the remaining thirteen stations, there is no trend in all time scales. The hydrological drought trend analysis in the basin on a seasonal (SDI3) and yearly (SDI12) time scale also revealed that three streamflow stations have a positive trend (Kessie, Gummera, and Border). This implies that water resource management is still a vital tool for the sustainable development of the Abbay river basin in the future.
PubDate: Mon, 28 Nov 2022 13:05:01 +000
- The Characteristics of Thunderstorms and Their Lightning Activity on the
Qinghai-Tibetan Plateau
Abstract: This paper discusses the temporal and spatial distribution characteristics of cloud-to-ground (CG) lightning activity over the Qinghai-Tibetan Plateau (QTP) from 2009 to 2018 and their dependence on meteorological factors. It is found that (1) the number of CG flashes fluctuates, reaches a maximum in 2014, and then gradually decreases. The main active period of CG lightning is from June to September each year, after which it decreases rapidly. CG lightning is mainly distributed in the valley areas at around 4800 m above sea level at Lhasa, Nagqu, and Chamdo, and there are differences in the characteristics of CG activity in these three areas. The peak of daily CG lightning occurs at 1000 UTC, and the lowest value is at 0400 UTC. The distribution of CG lightning in all seasons has obvious differences in peak time and the proportion of positive CG (+CG) lightning, with the ratio of +CG lightning to total CG lightning flashes in spring and autumn exceeding 50%. (2) The ratio of +CG lightning to total CG lightning flashes over the QTP is influenced by a combination of thermodynamic and microphysical factors. Over the QTP, greater vertical wind shear leads to the movement of upper positive charges and promotes the occurrence of +CG lightning. Also, the higher total column liquid water content implies higher cloud water content in the warm-cloud region, and the higher cloud-base height implies a thicker warm-cloud region, which is not conducive to the occurrence of +CG lightning. (3) During high-value years (in this study, 2010, 2012, 2014, and 2016), the midlatitude (30°N–60°N) high pressure is strong and the plateau is situated at the intersection of the East Asian and South Asian monsoons and the cold air from the northwest, which strengthens the water vapor convergence and increases the frequency of thunderstorms. When the plateau is under the control of the southerly monsoon from June to September every year, its atmosphere is full of water vapor and lightning activity is accordingly high, with the proportion of +CG lightning being about 10%. Meanwhile, in the remaining months, when controlled by the westerly wind belt, the plateau’s water vapor condition is poor, the level of lightning activity weakens, and the proportion of +CG lightning gradually increases to more than 50%.
PubDate: Fri, 28 Oct 2022 14:20:01 +000
- Modifying Covariance Localization to Mitigate Sampling Errors from the
Ensemble Data Assimilation
Abstract: The ensemble-based Kalman filter requires at least a considerable ensemble (e.g., 10,000 members) to identify relevant error covariance at great distances for multidimensional geophysical systems. However, increasing numerous ensemble sizes will enlarge sampling errors. This study proposes a modified Cholesky decomposition based on the covariance localization (CL) scheme, namely a covariance localization scheme with modified Cholesky decomposition (CL-MC). Our main idea utilizes a modified Cholesky (MC) decomposition technique for estimating the background error covariance matrix; meanwhile, we employ the tunable singular value decomposition method on the background error covariance to improve the ensemble increment and avoid the imbalance of the system. To verify if the proposed method can effectively mitigate the sampling errors, numerical experiments are conducted on the Lorenz-96 model and large-scale model (SPEEDY model). The results show that the CL-MC method outperforms the CL method for different data assimilation parameters (ensemble sizes and localizations). Furthermore, by performing one year of assimilation experiments on the SPEEDY model, it is found that the 1-day forecast RMSEs obtained by the CL are approximately equal to the 5-day forecast RMSEs of CL-MC. So, the CL-MC method has potential advantages for long-term forecasting. Maybe the proposed CL-MC method achieves good prospects for widespread application in atmospheric general circulation models.
PubDate: Wed, 26 Oct 2022 12:05:02 +000
- Study on the Precursor Signal Capturing of Unfavorable Weather:
Months/Years in Advance to Ultra-Early Forecast for Hourly Transient
Weather Changes during the Beijing Winter Olympics
Abstract: Today, among the existing numerical weather prediction models, those detailing target classifications have been sufficiently explored; however, there are still many weather forecasting goals and needs, and research from theoretical to practical methods still needs additional study. For example, it is important to know as early as possible (months to years in advance) the forecast during a “specific large public event,” such as the hourly weather forecast for the Olympic Games. This study elaborates on the theory and methods for such ultra-early prediction of severe transient weather processes in the atmosphere. The main results of this study include (1) establishing the academic concept to capture precursor signals in modern meteorology and provide definitions; (2) establishing methods for capturing precursory signal quantification of unfavorable weather and proposing quantitative measurable thresholds; and (3) proposing the “ultra-early prediction” target task. A typical case is discussed: the meteorological conditions of the Beijing Winter Olympics, which serves as an example of social demand for weather forecasting of “special large-scale public activities,” as the case results show that the real-time observations during the Beijing Winter Olympics are consistent with the forecast and followed the precursor signal developed using the theoretical and methodological approaches in this study. The numerical quantization indicators for precursor signals include: (1) for a decrease in the height of the mixed layer hidden in the diurnal change; the precursor signal threshold is defined as a drop of more than 100 m for 3 consecutive days; (2) the signal of the δΘe displayed as a change by “negative ⟶ positive” of more than seven days in a continuous period. (3) the supersaturation (S) with thresholds reaching 6–7%, as well as the threshold
PubDate: Wed, 19 Oct 2022 07:05:01 +000
- Local-Scale Weather Forecasts over a Complex Terrain in an Early Warning
Framework: Performance Analysis for the Val d’Agri (Southern Italy) Case
Study
Abstract: Forecasting applications based on hourly meteorological predictions for weather variables are nowadays used in energy market operations, planning of gas and power supply, and renewable energy, among others. Available meteorological and climatological data, as well as critical thresholds of rainfall, may also have a key role in the hazard classification, related to slope instabilities of pipelines and critical infrastructures along routes. The present study concerns the performance of a weather forecast model in the framework of an early warning system (EWS) application, which supports the integrity management of oil and gas pipelines. This EWS has been applied on to a specific area: the Val d’Agri basin in the Basilicata region of Southern Italy, which is extensively affected by several landslides and floods. The hourly precipitation forecasts are provided by a dedicated meteorological model, the KALM-HD, using two different horizontal resolutions, 1.25 and 5 km, to analyze possible influences of the mesh grid size as well. On this area, several weather stations were specifically deployed to obtain observed data in a region where hydrogeological hazards are relevant for asset management. A comparison among observations and the KALM-HD scaled forecasts on six of these weather stations is presented to assess the model performance. Besides, precipitation, temperature, and wind speed are evaluated as well. The forecasting analysis is performed considering two years of data both on an overall and seasonal basis. Results show that the KALM-HD performs well with the 1.25 km grid, particularly on temperature and wind speed variables. Since weather stations can be gathered in two main sets depending on their positions, differences arise in the forecast quality of these two groups, related to orography and thermal effects, whose detection is difficult in the typical narrow valleys characterizing the area of study. This issue prevalently influences temperatures and local winds, which, these latter, are generally underestimated, while precipitation is mainly driven by synoptic circulation and its interaction with mesoscale meteorological features.
PubDate: Wed, 19 Oct 2022 05:50:01 +000
- Improving Wind Speed Forecasting for Urban Air Mobility Using Coupled
Simulations
Abstract: Hazardous weather, turbulence, wind, and thermals pose a ubiquitous challenge to Unmanned Aircraft Systems (UAS) and electric-Vertical Take-Off and Landing (e-VTOL) aircrafts, and the safe integration of UAS into urban area requires accurate high-granularity wind data especially during landing and takeoff phases. Two models, namely, Open-Source Field Operation and Manipulation (OpenFOAM) software package and Weather Research and Forecasting (WRF) model, are used in the present study to simulate airflow over Downtown Oklahoma City, Oklahoma, United States. Results show that computational fluid dynamics wind simulation driven by the atmospheric simulation significantly improves the simulated wind speed because the accurate modeling of the buildings affects wind patterns. The evaluation of different simulations against six Micronet stations shows that WRF-CFD numerical evaluation is a reliable method to understand the complicated wind flow within built-up areas. The comparison of wind distributions of simulations at different resolutions shows better description of wind variability and gusts generated by the urban flows. Simulations assuming anisotropy and isotropy of turbulence show small differences in the predicted wind speeds over Downtown Oklahoma City given the stable atmospheric stratification showing that turbulent eddy scales at the evaluation locations are within the inertial subrange and confirming that turbulence is locally isotropic.
PubDate: Wed, 12 Oct 2022 11:50:01 +000
- Comparison of the Applicability of Two Reanalysis Products in Estimating
Tall Tower Wind Based on Multiple Linear Regression and Artificial Neural
Network in South China
Abstract: Climate reanalysis products have been widely used to overcome the absence of high-quality and long-term observational records for wind energy users. In this study, the applicability of two popular reanalysis datasets (ERA5 and MERRA2) in estimating wind characteristics for four tall tower observatories (TTOs) in South China was assessed. For each TTO, linear and nonlinear downscaling techniques, namely, multiple linear regression (MLR) and an artificial neural network (ANN), respectively, were adopted for the downscaling of the scalar wind speed and the corresponding U/V components. The downscaled wind speed and U/V components were subsequently compared with the TTO observations by correlation coefficient (Pearson’s r), the root mean square error (RMSE), the uncertainty analysis (U95), and the reliability analysis (RE). According to the results, ERA5 had a better applicability (higher Pearson’s r and RE, but lower RMSE and U95) in estimating TTO wind speed than MERRA2 when using both the MLR and ANN downscaling method. The average Pearson’s r, RE, RMSE, and U95 of the downscaled wind from ERA5 by the MLR (ANN) method were 0.66 (0.69), 40.8% (41.8%), 2.20 m/s (2.11 m/s), 0.181 m/s (0.179 m/s), respectively, and 0.60 (0.63), 38.0% (39.7%), 2.32 m/s (2.25 m/s), 0.189 m/s (0.187 m/s), respectively, for MERRA2. The wind components analysis showed that the better performance of ERA5 was attributed to its smaller error in estimating V component than MERRA2. For the wind direction, the two reanalysis datasets did not display distinct differences. Additionally, the misalignment of the wind direction between the reanalysis products and the TTOs was higher for the secondary predominant wind direction (SPWD) than for the predominant wind direction (PWD). Furthermore, we found that the reanalysis U wind had a higher RMSE but a lower RE and Pearson’s r than the V wind, which indicates that the misalignment in the wind direction was mainly associated with the deviation in the U component.
PubDate: Tue, 11 Oct 2022 14:05:01 +000
- Variation of Leaf Area Index (LAI) under Changing Climate: Kadolkele
Mangrove Forest, Sri Lanka
Abstract: Mangroves are an essential plant community in coastal ecosystems. While the importance of mangrove ecosystems is well acknowledged, climate change is expected to have a considerable negative impact on them, especially in terms of temperature, precipitation, sea level rise (SLR), ocean currents, and increasing storminess. Sri Lanka ranks near the bottom of the list of countries researching this problem, even though the scientific community's interest in examining the variation in mangrove health in response to climate change has gained significant attention. Consequently, this study illustrates how the leaf area index, a measure of mangrove health, fluctuates in response to varying precipitation, particularly during droughts in Sri Lanka's Kadolkele mangrove forest. The measurements of the normalized difference vegetation index (NDVI) were used to produce the leaf area index (LAI), which was then combined with the standard precipitation index (SPI) to estimate the health of the mangroves. The climate scenario, RCP8.5, was used to forecast future SPI (2021–2100), and LAI was modeled under the observed (1991–2019) and expected (2021–2100) drought events. The study reveals that the forecasted drought intensities modeled using the RCP8.5 scenario have no significant variations on LAI, even though some severe and extreme drought conditions exist. Nevertheless, the health of the mangrove ecosystem is predicted to deteriorate under drought conditions and rebound when drought intensity decreases. The extreme drought state (-2.05) was identified in 2064; therefore, LAI has showcased its lowest (0.04). LAI and SPI are projected to gradually increase from 2064 to 2100, while high fluctuations are observed from 2021 to 2064. Limited availability of LAI values with required details (measured date, time, and sample locations) and cloud-free Landsat images have affected the study results. This research presents a comprehensive understanding of Kadolkele mangrove forest under future droughts; thus, alarming relevant authorities to develop management plans to safeguard these critical ecosystems.
PubDate: Mon, 10 Oct 2022 15:50:02 +000
- Characterization of Local Climate and Its Impact on Faba Bean (Vicia faba
L.) Yield in Central Ethiopia
Abstract: Climate change is a major threat to agricultural production and undermines the efforts to achieve sustainable development goals in poor countries such as Ethiopia that have climate-sensitive economies. The objective of this study was to assess characterization of local climate and its impact on productivity faba bean (Vicia faba L.) varieties (Gora and Tumsa) productivity in Welmera watershed area, central Ethiopia. Historical climate (1988–2017) and eight years of crop yield data were obtained from National Meteorological Agency of Ethiopia and Holeta Agricultural Research Center. Trend, variability, correlation, and regression analyses were carried out to characterize the climate of the area and establish association between faba bean productivity and climate change. The area received mean annual rainfall of 970 mm with SD of 145.6 and coefficient of variation (CV %) of 15%. The earliest and latest onset of rainfall were April 1 (92 DOY) and July 5 (187 DOY), whereas, the end date of rainy season was on September 2 (246 DOY) and October 31 (305 DOY), respectively. The average length of the growing period was 119 days, with a CV% of 35.2%. The probability of dry spell less than 7 days was high (>80%) until the last decade of May (151 DOY); however, the probability sharply declined and reached 0% on the first decade of July (192 DOY). Kiremt (long rainy season that occurs from June to September) and belg (short rainy season that falls from February to April/May) rainfall had increasing trends at a rate of 4.7 mm and 2.32 mm/year, respectively. The annual maximum temperature showed increasing trend at a rate of 0.06°C per year and by a factor of 0.34°C, which is not statistically significant. The year 2014 was exceptionally drought year while 1988 was wettest year. Kiremt (JJAS) start of rain and rainy day had strong correlation and negative impact on Gora yield with (r = −0.407 and −0.369), respectively. The findings suggests large variation in rainfall and temperature in the study area which constraints faba bean production. Investment on agricultural sector to enhance farmer’s adaptation capacity is essential to reduce the adverse impacts of climate change and variability on faba bean yield. More research that combines household panel data with long-term climate data is necessary to better understand climate and its impact on faba bean yield.
PubDate: Sat, 08 Oct 2022 17:35:01 +000
- Interannual Variations of Water and Carbon Dioxide Fluxes over a Semiarid
Alpine Steppe on the Tibetan Plateau
Abstract: Water and carbon exchanges between grassland and the atmosphere are important processes for water balance and carbon balance. Based on eddy covariance observations over a semiarid alpine steppe ecosystem in Bange on the Tibetan Plateau during the growing season from 2014 to 2017, the variations in evapotranspiration (ET), net ecosystem exchange (NEE), and their components and the associated driving factors were analyzed. Linear and nonlinear models were applied to investigate the relationships between fluxes and their controlling factors over different timescales. The results show that the average ET for the growing season ranged from 1.1 to 2.4 mm/d with an average of 2.0 mm/d for the four consecutive years. Drought conditions reduced the surface conductance and hence the Priestley–Taylor coefficient. Mean T/ET was low (0.34) due to low vegetation cover. Plant growth increased the T/ET ratio during the growing season, whereas soil water content (SWC) explained most of the variation of ET and E on daily and monthly scales. The Enhanced Vegetation Index (EVI) was the most important controlling factor for temperature. Transpiration increased with SWC in dry conditions. For the growing season in 2014, 2016, and 2017, Bange was a carbon sink, while it was a carbon source in 2015. The largest CO2 flux was higher and the temperature sensitivity coefficient (Q10) was lower for 2015 than for the other three years. SWC affected these photosynthesis and respiration parameters. The ratio of respiration (Re) to gross primary production (GPP) was the highest during the 2015 growing season. Both on daily and monthly scales, Re was positively and linearly correlated with GPP. The most important controlling factor for the CO2 flux was EVI on daily and monthly scales.
PubDate: Thu, 06 Oct 2022 06:35:01 +000
- The Influence of Data Length on the Performance of Artificial Intelligence
Models in Predicting Air Pollution
Abstract: Air pollution is one of humanity's most critical environmental issues and is considered contentious in several countries worldwide. As a result, accurate prediction is critical in human health management and government decision-making for environmental management. In this study, three artificial intelligence (AI) approaches, namely group method of data handling neural network (GMDHNN), extreme learning machine (ELM), and gradient boosting regression (GBR) tree, are used to predict the hourly concentration of PM2.5 over a Dorset station located in Canada. The investigation has been performed to quantify the effect of data length on the AI modeling performance. Accordingly, nine different ratios (50/50, 55/45, 60/40, 65/35, 70/30, 75/25, 80/20, 85/15, and 90/10) are employed to split the data into training and testing datasets for assessing the performance of applied models. The results showed that the data division significantly impacted the model's capacity, and the 60/40 ratio was found more suitable for developing predictive models. Furthermore, the results showed that the ELM model provides more precise predictions of PM2.5 concentrations than the other models. Also, a vital feature of the ELM model is its ability to adapt to the potential changes in training and testing data ratio. To summarize, the results reported in this study demonstrated an efficient method for selecting the optimal dataset ratios and the best AI model to predict properly which would be helpful in the design of an accurate model for solving different environmental issues.
PubDate: Fri, 30 Sep 2022 11:05:02 +000
- Spatio-Temporal Rainfall Variability and Concentration over Sri Lanka
Abstract: Changes in precipitation patterns significantly affect flood and drought hazard management and water resources at local to regional scales. Therefore, the main motivation behind this paper is to examine the spatial and temporal rainfall variability over Sri Lanka by Standardized Rainfall Anomaly Index (SRAI) and Precipitation Concentration Index (PCI) from 1990 to 2019. The Mann–Kendall (MK) trend test and Sen’s slope (SS) were utilized to assess the trend in the precipitation concentration based on PCI. The Inverse Distance Weighting (IDW) interpolation method was incorporated to measure spatial distribution. Precipitation variability analysis showed that seasonal variations are more than those of annual variations. In addition, wet, normal, and dry years were identified over Sri Lanka using SRAI. The maximum SRAI (2.27) was observed for the year 2014 for the last 30 years (1990–2019), which shows the extremely wet year of Sri Lanka. The annual and seasonal PCI analysis showed moderate to irregular rainfall distribution except for the Jaffna and Ratnapura areas (annual scale-positive changes in Katugastota for 21.39% and Wellawaya for 17.6%; seasonal scale-Vavuniya for 33.64%, Trincomalee for 31.26%, and Batticaloa for 18.79% in SWMS). The MK test, SS-test, and percent change analyses reveal that rainfall distribution and concentration change do not show a significant positive or negative change in rainfall pattern in Sri Lanka, despite a few areas which experienced significant positive changes. Therefore, this study suggests that the rainfall in Sri Lanka follows the normal trend of precipitation with variations observed both annually and seasonally.
PubDate: Wed, 28 Sep 2022 11:05:03 +000
- Blue-Green Space Changes of Baiyangdian Wetland in Xiong’an New
Area, China
Abstract: As a regulator of ecological environment, Baiyangdian Wetland is in a pivotal position in constructing the blue-green space (BGS) of Xiong’an New Area in China. This study aims to reveal the spatiotemporal changes of the BGS in Baiyangdian Wetland from 2016 to 2021. It uses Google Earth Engine (GEE) to calculate NDVI and NDWI based on Sentinel-2 Satellite remote sensing data and extracts the blue-green space by a classification model driven by NDVI and NDWI. Moreover, the land-use transfer matrix and landscape pattern indices are applied for evaluating the BGS changes in the wetland. According to the results, vegetation in the wetland shows no obvious spatial transfer. From 2016 to 2020, the BGS proportion in the wetland showed a stable increase, with the blue space getting larger by 10.8%. The indicators of the Number of Patches (NP), Patch Density (PD), Largest Patch Index (LPI), Contagion, and Landscape Shape Index (LSI) of the wetland decreased, suggesting a better ecological environment since the establishment of Xiong’an New Area in 2017. Based on the results, the author makes the following conclusion: the construction of BGS in Baiyangdian Wetland results in a well-organized ecological environment. The study provides a reference for building Xiong’an New Area and monitoring BGS changes in other regions.
PubDate: Tue, 27 Sep 2022 12:35:03 +000
- The Space Conceptual Models and Water Vapor Characteristics of Typical
Rainstorms during Plum Rain Season
Abstract: Based on conventional observation data from the China Meteorological Administration (CMA) and reanalysis data from the American National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) between 2012 and 2021, combined with the meteorological analysis, composite synthesis, and water vapor trajectory analysis, the weather circulations of typical rainstorms during the 10 years can be divided into 4 categories: Static Front Pattern (SFP), Subtropical High Edge Pattern (SHEP), Northeast Cold Vortex Pattern (NCVP), and Low-Level Vortex and Shear Pattern (LLVSP). The SHEP and SFP rainstorms have the characteristics of long duration and wide range, while the NCVP rainstorms are characterized by mobility and disaster weather accompaniment. The daily precipitation of LLVSP cases has extremity feature. The occurrence and development of rainstorms are well coordinated with the systems on lower levels. The main water vapor channel in lower layers of the SFP cases is from the South China Sea, while it is from Bohai for the NCVP cases and the Bay of Bengal for the SHEP and LLVSP cases. The main water vapor channel in middle layers is from the Bay of Bengal because of the affection of the southwest air flow. The south boundary of the MLYRB is the most important water vapor input boundary, followed by the west boundary, while the East and North boundaries are the outflow boundaries. During the rainstorms, the low-level water vapor is exuberant with low-level water vapor convergence much stronger than the high-level divergence. Among the four types of rainstorms, the NCVP cases provide the most abundant low-level water vapor convergence, resulting in the strongest short-term precipitation among the four types. Combined with water vapor transportation and convergence, the refined spatial conceptual models of the four types of rainstorms can better judge the process intensity and falling area and provide reference for disastrous weather forecast and early warning.
PubDate: Mon, 26 Sep 2022 15:05:03 +000
- Frost Forecasting considering Geographical Characteristics
Abstract: Regional accuracy was examined using extreme gradient boosting (XGBoost) to improve frost prediction accuracy, and accuracy differences by region were found. When the points were divided into two groups with weather variables, Group 1 had a coastal climate with a high minimum temperature, humidity, and wind speed and Group 2 exhibited relatively inland climate characteristics. We calculated the accuracy in the two groups and found that the precision and recall scores in coastal areas (Group 1) were significantly lower than those in the inland areas (Group 2). Geographic elements (distance from the nearest coast and height) were added as variables to improve accuracy. In addition, considering the continuity of frost occurrence, the method of reflecting the frost occurrence of the previous day as a variable and the synthetic minority oversampling technique (SMOTE) pretreatment were used to increase the learning ability.
PubDate: Sun, 25 Sep 2022 17:35:02 +000
- Variation in Surface Solar Radiation and the Influencing Factors in
Xinjiang, Northwestern China
Abstract: The variation of solar radiation has a profound effect on the surface energy balance and hydrological cycle. Although the relationship between solar radiation variation and its influencing factors has been extensively studied, they are seldom used in Xinjiang, the largest province in China. In this study, we investigated the spatial distribution and temporal variation in global radiation (Eg), water vapor content (WVC), aerosol optical depth (AOD), total cloud cover (TCC), and low-level cloud cover (LCC) in Xinjiang, northwestern China, between 1961 and 2015. The annual average Eg reported at all stations was 5126.3–6252.8 MJ·m−2 with a mean of 5672 MJ·m−2. The highest annual mean Eg of 6252.8 MJ·m−2 occurred in Hami, eastern Xinjiang, whereas the lowest annual mean Eg of 5126.3 MJ·m−2 occurred in Urumqi, northern Xinjiang. The annual Eg variation was mainly affected by WVC, AOD, TCC, and LCC. Decreases in annual, spring, summer, autumn, and winter Eg trends were recorded in Xinjiang at rates of −33.88 × 10−2, −1.92 × 10−2, −1.89 × 10−2, −3.47 × 10−2, and −3.56 × 10−2 MJ·m−2·decade−1, respectively, with decreasing ratios of 9.43%, 5.85%, 0.14%, 8%, and 20.55%, respectively. Increasing trends in annual WVC, AOD, TCC, and LCC were noted in Xinjiang at rates of 7.12 × 10−5 mm·decade−1, 2.74 × 10−6 decade−1, 8.77 × 10−5 % decade−1, and 5.73 × 10−5% decade−1, respectively. In addition, increasing trends in the annual Eg at Yining and Yanqi stations were observed. The Eg spatial distribution was complex in Xinjiang at the stations observed in this study, which were divided into six groups. Eg at group 1 showed an increasing trend associated with decreases in the WVC and TCC, whereas decreases in Eg were observed at groups 2–6, which could have been influenced by increases in AOD, TCC, and LCC.
PubDate: Mon, 12 Sep 2022 12:05:00 +000
- Wavelet Analysis of the Interconnection between Atmospheric Aerosol Types
and Direct Irradiation over Cameroon
Abstract: The comparative analysis of the intra- and interannual dynamics between the Direct Normal Irradiation (DNI) under clear sky conditions and five aerosol types (Dust, Sea Salt, Black Carbon, Organic Carbon, and Sulfate) is the purpose of this study. To achieve this aim, we used fifteen-year DNI and aerosols data downloaded at 3-hour time intervals in nine defined zones throughout Cameroon. The wavelet transform is a powerful tool for studying local variability of amplitudes in a temporal dataset and constitutes our principal tool. The results show unequal distribution of aerosol types according to zones, but the Desert Dusts (DU) and Organic Carbon (OM) aerosols have been found as dominant particles in the studied region. The wavelet coherence analysis between DNI and each aerosol type reveals three bands of periodicity: 4-month band, 8–16-month band, and sometimes after-32-month band, with the most important frequency at 8–16-month band period. However, the intensity of coherence across bands varies with respect to aerosol type as well as each of the nine climate zones. A significant anticorrelation relationship was obtained between DNI and each type of aerosol, emphasizing that the presence of such atmospheric particles could dampen the renewable energy utilized by power systems. Also, the analysis shows that scattering aerosols such as Sulfate and Sea Salt (SU and SS, respectively) lead DNI in phase while absorbing aerosols such as Organic Carbon, Black Carbon, and Dust (OM, BC, and DU, respectively) give phase lag with DNI.
PubDate: Mon, 05 Sep 2022 23:50:01 +000
- Data-Driven versus Köppen–Geiger Systems of Climate
Classification
Abstract: Climate zone classification promotes our understanding of the climate and provides a framework for analyzing a range of environmental and socioeconomic data and phenomena. The Köppen–Geiger classification system is the most widely used climate classification scheme. In this study, we compared the climate zones objectively defined using data-driven methods with Köppen–Geiger rule-based classification. Cluster analysis was used to objectively delineate the world’s climatic regions. We applied three clustering algorithms—k-means, ISODATA, and unsupervised random forest classification—to a dataset comprising 10 climatic variables and elevation; we then compared the obtained results with those from the Köppen–Geiger classification system. Results from both the systems were similar for some climatic regions, especially extreme temperature ones such as the tropics, deserts, and polar regions. Data-driven classification identified novel climatic regions that the Köppen–Geiger classification could not. Refinements to the Köppen–Geiger classification, such as precipitation-based subdivisions to existing Köppen–Geiger climate classes like tropical rainforest (Af) and warm summer continental (Dfb), have been suggested based on clustering results. Climatic regions objectively defined by data-driven methods can further the current understanding of climate divisions. On the other hand, rule-based systems, such as the Köppen–Geiger classification, have an advantage in characterizing individual climates. In conclusion, these two approaches can complement each other to form a more objective climate classification system, wherein finer details can be provided by data-driven classification and supported by the intuitive structure of rule-based classification.
PubDate: Wed, 31 Aug 2022 05:50:00 +000
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