Subjects -> METEOROLOGY (Total: 112 journals)
 Showing 1 - 36 of 36 Journals sorted by number of followers Journal of Atmospheric and Solar-Terrestrial Physics       (Followers: 198) Nature Climate Change       (Followers: 133) Journal of the Atmospheric Sciences       (Followers: 80) Atmospheric Environment       (Followers: 73) Atmospheric Research       (Followers: 69) Climatic Change       (Followers: 64) Journal of Climate       (Followers: 54) Bulletin of the American Meteorological Society       (Followers: 51) Atmospheric Chemistry and Physics (ACP)       (Followers: 48) Climate Dynamics       (Followers: 44) Advances in Atmospheric Sciences       (Followers: 43) Climate Policy       (Followers: 42) Nature Reports Climate Change       (Followers: 37) Atmospheric Science Letters       (Followers: 36) Journal of Applied Meteorology and Climatology       (Followers: 35) Journal of Atmospheric and Oceanic Technology       (Followers: 34) Monthly Weather Review       (Followers: 34) International Journal of Climatology       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and Oceanography       (Followers: 7) Climate       (Followers: 6) Open Journal of Modern Hydrology       (Followers: 6) Mathematics of Climate and Weather Forecasting       (Followers: 6) Journal of the Meteorological Society of Japan       (Followers: 6) Climate Research       (Followers: 6) Current Climate Change Reports       (Followers: 6) Aeolian Research       (Followers: 6) The Cryosphere (TC)       (Followers: 5) Climate Risk Management       (Followers: 5) Dynamics and Statistics of the Climate System       (Followers: 5) Climate of the Past (CP)       (Followers: 5) Change and Adaptation in Socio-Ecological Systems       (Followers: 5) Urban Climate       (Followers: 4) International Journal of Environment and Climate Change       (Followers: 4) Environmental and Climate Technologies       (Followers: 4) Carbon Balance and Management       (Followers: 4) Journal of Integrative Environmental Sciences       (Followers: 4) The Cryosphere Discussions (TCD)       (Followers: 4) Weatherwise       (Followers: 4) Meteorological Applications       (Followers: 4) Climate Services       (Followers: 3) Meteorologische Zeitschrift       (Followers: 3) Atmósfera       (Followers: 3) Russian Meteorology and Hydrology       (Followers: 3) Journal of Climatology       (Followers: 3) Ciencia, Ambiente y Clima       (Followers: 3) Frontiers in Climate       (Followers: 3) Economics of Disasters and Climate Change       (Followers: 3) Acta Meteorologica Sinica       (Followers: 3) Atmospheric Environment : X       (Followers: 3) npj Climate and Atmospheric Science       (Followers: 3) Journal of Weather Modification       (Followers: 2) Open Atmospheric Science Journal       (Followers: 2) GeoHazards       (Followers: 2) Journal of Climate Change       (Followers: 2) Climate and Energy       (Followers: 2) International Journal of Image and Data Fusion       (Followers: 2) Meteorologica       (Followers: 2) Climate Summary of South Africa       (Followers: 2) Meteorological Monographs       (Followers: 1) 气候与环境研究       (Followers: 1) Journal of Meteorological Research       (Followers: 1) Bulletin of Atmospheric Science and Technology       (Followers: 1) Michigan Journal of Sustainability       (Followers: 1) Tropical Cyclone Research and Review       (Followers: 1) International Journal of Biometeorology       (Followers: 1) Modeling Earth Systems and Environment       (Followers: 1) Mediterranean Marine Science       (Followers: 1) Large Marine Ecosystems       (Followers: 1) Weather and Climate Dynamics Journal of Agricultural Meteorology Nīvār Revista Iberoamericana de Bioeconomía y Cambio Climático Mètode Science Studies Journal : Annual Review Earth Perspectives - Transdisciplinarity Enabled Climate of the Past Discussions (CPD) Revista Brasileira de Meteorologia Studia Geophysica et Geodaetica
Similar Journals
 Meteorology and Atmospheric PhysicsJournal Prestige (SJR): 0.543 Citation Impact (citeScore): 1Number of Followers: 26      Hybrid journal (It can contain Open Access articles) ISSN (Print) 1436-5065 - ISSN (Online) 0177-7971 Published by Springer-Verlag  [2653 journals]
• An evaluation of Global Satellite Mapping of Precipitation (GSMaP)
datasets over Iran
• Abstract: This study aimed to quantitatively evaluate three GSMaP products including near-real-time (GSMaP-NRT), microwave-infrared reanalyzed (GSMaP-MVK), and gauge-adjusted (GSMaP-Gauge) data with a spatial resolution of 0.1° × 0.1° versus gauge-observed data (observation) at daily and monthly time scales over Iran. Different statistical metrics including correlation coefficient (R), percent bias (PBias), root mean square error (RMSE), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) were used to evaluate the capability of the GSMaP products. The results indicated that all three products were generally capable of capturing the spatial pattern of precipitation. However, they all overestimated precipitation in general, and there was a considerable difference between the two satellite-only products GSMaP-MVK and GSMaP-NRT and the gauge-corrected product GSMaP-Gauge. GSMaP-Gauge performed better than the other products at both daily and monthly time scales. The evaluations demonstrated that all the GSMaP products provided better estimations of precipitation over Western than Eastern Iran. The strongest agreement between the products and the observed data was observed for the rainy months, while performance was poor for the dry months. The findings could help GSMaP product developers to better understand the characteristics of the involved errors.
PubDate: 2021-03-11

• Support vector regression integrated with novel meta-heuristic algorithms
for meteorological drought prediction
• Abstract: Drought is a complex natural phenomenon, so, precise prediction of drought is an effective mitigation tool for measuring the negative consequences on agriculture, ecosystems, hydrology, and water resources. The purpose of this research was to explore the potential capability of support vector regression (SVR) integrated with two meta-heuristic algorithms i.e., Grey Wolf Optimizer (GWO), and Spotted Hyena Optimizer (SHO), for meteorological drought (MD) prediction by utilizing EDI (effective drought index). For this objective, the two-hybrid SVR–GWO, and SVR–SHO models were constructed at Kumaon and Garhwal regions of Uttarakhand State (India). The EDI was computed in both study regions by using monthly rainfall data series to calibrate and validate the advanced hybrid SVR models. The autocorrelation function (ACF) and partial-ACF (PACF) were utilized to determine the optimal inputs (antecedent EDI) for EDI prediction. The results produced by the hybrid SVR models were compared with the calculated (observed) values by employing the statistical indicators and through graphical inspection. A comparison of results demonstrates that the hybrid SVR–GWO model outperformed to the SVR–SHO models for all study stations located in Kumaon and Garhwal regions. Also, the results highlighted the better suitability, supremacy, and convergence behavior of meta-heuristic algorithms (i.e., GWO and SHO) for meteorological drought prediction in the study regions.
PubDate: 2021-03-08

• Assessment of the v2016 NWCSAF CRR and CRR-Ph precipitation estimation
performance over the Greek area using rain gauge data as ground truth
• Abstract: The NWCSAF (Support to Nowcasting and Very Short Range Forecasting Satellite Application Facility) software package provides operational products that ensure the optimum use of meteorological satellite data in Nowcasting and Very Short Range Forecasting. The National Observatory of Athens operates NWCSAF since 2016. The rainfall estimates obtained by the Convective Rainfall Rate (CRR) nighttime algorithm and the Convective Rainfall Rate from Cloud Physical Properties (CRR-Ph) algorithm of the 2016 version are verified against rainfall observations provided by the dense network of automated surface weather stations operated by the National Observatory of Athens (NOA) for a full year. For the verification a temporal upscaling to 30 min was applied to all datasets. Overall, CRR overestimates the extent of the precipitation areas while at the same time it underestimates the precipitation totals. CRR-Ph clearly outperforms the CRR nighttime algorithm regarding the accurate delineation of precipitation areas but it overestimates the precipitation totals. Heavier precipitation is consistently detected by both algorithms although the false alarms rate is high. Seasonal variations are found, with the most important the poorer estimation performance during spring.
PubDate: 2021-02-26

• Evaluation of 3D structural changes in general atmospheric and monsoon
circulations during Kedarnath disaster (India), 16–17 June 2013
• Abstract: Intense rains on 16–17 June 2013 over Mandakini River catchment caused Kedarnath disaster in Uttarakhand State (India). Normal and departure-from-normal 3D global atmospheric thermal structure and general and monsoon circulations during the event have been compared using equatorially/globally- conditioned surface and upper air parameters (1000–100 hPa) (period: 1979–2013). Briefly, normal monsoon structure is: 1000‒850 hPa layer- cross-equatorial flows over Indian Ocean and Eurasian westerlies confluence over Indian domain, the two flows further confluence downstream with Pacific easterlies and then the accumulated airmasses blow northeastward; 700‒500 hPa layer- Eurasian westerlies after sweeping entire Indian subcontinent make exit northeastward; and 400‒100 hPa layer- upper tropospheric anticyclone well-developed over subtropical Asia and outflows are spread all around. On 16–17 June 2013, two upper tropospheric anticyclonic cells occurred, one over Tibet-China and another Mediterranean-Middle East. Troposphere (1000–250 hPa) was significantly warmer-and-thicker over Tibet-China followed by Mediterranean-Middle East while cooler-and-thinner over central Asia-India sector. Departures in downward slopes of tropospheric temperature and thickness from Tibet-China outward were significantly steeper. A huge trough evolved over Indo-Pacific region from an interaction between Eurasian westerlies and Indo-Pacific easterlies, and outflows from the trough made forced exit through western Himalaya which modulated north mid-high latitudes westerlies into a single wave structure. Combined five factors produced disastrous rains over Kedarnath: cool-low and warm-low regime contrast; squeezing of deep warm-moist flows; orographic lifting; and pumping and suction effects. Forced warm-moist outflows from Indo-Pacific region caused warmer-thicker troposphere over eastern Russia-North America. Lesser outflows from Tibet-China anticyclone were directed southward, consequently troposphere was cooler-thinner over southern mid-high latitudes.
PubDate: 2021-02-25

• High-order conservative and oscillation-suppressing transport on irregular
hexagonal grids
• Abstract: A third-order numerical scheme was developed for 2D irregular hexagonal meshes for the advection problems in this study. The scheme is based on a multi-moment constrained finite-volume method (MCV) in Cartesian coordinates and entails the introduction of a general integration method over a hexagonal cell. Unlike in the conventional finite-volume method, various discrete moments, that is, point value and volume-integrated average, are adopted as computational constraints to achieve high-order computation. The high-order spatial reconstruction can therefore be built in a local space, which considerably reduces the stencil length. The numerical scheme is tested using various idealized experiments. Compared with the existing schemes, this scheme is demonstrated to be flexible for application in irregular hexagonal meshes without increasing cost or compromising on accuracy. The general integration formulation based on a third-order polynomial helps to expand the application to arbitrary hexagons that does not require the use of centroids as computational points or Voronoi tessellation. It is also convenient to define the orthogonal wind components in the Cartesian system to directly drive the atmospheric transport.
PubDate: 2021-02-24

• Assessing current and future spatiotemporal precipitation variability and
trends over Uganda, East Africa, based on CHIRPS and regional climate
model datasets
• Abstract: The lack of reliable rainfall projection records remains a major challenge to Uganda. In the advent of extreme wetness or drought events, reliable rainfall estimates for local planning and adaptation are essential. The present study used two main datasets to conduct a historical analysis from 1981 to 2019, coupled with future projections under representative concentration pathway (RCP 8.5) for the period 2020–2050. Historical analysis revealed bimodal annual rainfall patterns for March–May (MAM) and September–November (SON) gradients representing heavier to lighter rainfall events, respectively, over the study area. Investigation of recent trends in rainfall patterns revealed an upward trend from 2010 onwards in annual and seasonal rainfall. Moreover, results for future projections show wet conditions are projected to occur over the study area between the months of April/May and October. Contrarily, March is likely to experience a reduction in wet conditions. Mann–Kendall test employed to make future projections of rainfall depicted decreasing patterns during MAM season whilst increasing tendencies with strong shift was highlighted for SON season over the study region. Meanwhile, annual projections indicate huge variations with linear trends showing a marginal increase as compared to historical trends. Findings would serve as baseline print to propel further studies that could delve into impact analysis of drought extreme events which pose significant threats to the agricultural sector which is heavily reliant on rainfall.
PubDate: 2021-02-22

• Artificial intelligence in forecasting central pressure drop and maximum
sustained wind speed of cyclonic systems over Arabian Sea: skill
comparison with conventional models
• Abstract: An attempt is made in this study to forecast the central pressure drop (PD) and maximum sustained wind speed (MSWS) associated with cyclonic systems at the stage of the highest intensity over Arabian Sea using artificial neural network models. The cyclonic systems include the phases from deep depression to extreme severe cyclones. The sea surface temperature, mid-tropospheric relative humidity, surface to middle tropospheric equivalent potential temperature gradient, inverse of wind shear and vertical wind velocity at 200 hPa level are evaluated as the most suitable predictors through principal component analysis. Various neural network models with different architectures have been trained with the data from 1990 to 2012 to select the best forecast model. The prediction skill of the intelligent models is evaluated by different accuracy measures. The results show that the multi-layer perceptron (MLP) model with five input layers, one hidden layer with four nodes and one output layer is the best model for forecasting PD with minimum prediction error of 0.14 at 36 h lead time, whereas the MLP model with five input layers, one hidden layer with five nodes and one output layer is found to be the best model for forecasting MSWS with minimum prediction error of 0.19 at 48 h (h) lead time. The results are well validated with the observations from 2013 to 2018. The forecast skill of MLP model is compared with multiple linear regression model and existing operational and numerical weather prediction models.
PubDate: 2021-02-18

• Prediction of tropical cyclone trajectories over the Northern Indian Ocean
using COSMO
• Abstract: The present study investigates the performance of a regional numerical weather prediction model; namely, the Consortium for Small-scale Modelling (COSMO) in the prediction of the tropical cyclone (TC) trajectories for varying intensities of the storm. A total of 8 TCs formed over the Northern Indian Ocean from 2017 to 2019 are chosen for the evaluation of the COSMO model. The central pressure ( $$P_\mathrm{Central}$$ ), pressure drop ( $$\varDelta P$$ ), and maximum sustained surface wind speed (MSW) simulated by the COSMO model are validated against the concurrent observations from India Meteorological Department. The forecasted mean track errors are 95 km for a lead time of 24 h, whereas it was about 140 km for a lead time of 48 h. The mean initial positional error in identification of the storm was about 50 km. The intensity of a storm is underestimated in terms of $$\varDelta P$$ and MSW, especially for a lead time of 0–24 h, whereas the model shows a consistent overestimation for a lead time of more than 24 h. During the initial stage of a storm, when its intensity is categorized as a Deep Depression, we notice a maximum amount of uncertainty in the prediction of cyclone track. The COSMO model yields improved predictability of the tracks for storms categorized as Very Severe Cyclonic Storms. As the intensity of a storm increases from a Deep Depression to a Very Severe Cyclonic Storm, the track errors associated with model simulations tend to decrease. Results of the present study illustrate the predictability of TCs from COSMO in terms of the trajectory and intensity of the storm.
PubDate: 2021-02-17

• The effect of the differences in near-infrared water vapour continuum
models on the absorption of solar radiation
• Abstract: There are currently significant disagreements in the strength of the water vapour continuum in the near-infrared region. To understand the effects of these disagreements on the absorption of solar radiation, line-by-line radiative transfer calculations were performed from 2000 to 10,000 cm−1 (1–5 μm) for three standard atmospheres; tropical, mid-latitude summer and sub-arctic winter atmospheres. These calculations were carried out at a solar zenith angle of 60° using line parameters from HITRAN (HIgh-resolution TRANsmission). Three currently available water vapour continuum models were selected for this study; versions 2.5 and 3.2 of the semi-empirical MT_CKD (Mlawer-Tobin-Clough-Kneizys-Davies) model and the laboratory-measured CAVIAR (Continuum Absorption at Visible and Infrared Wavelengths and its Atmospheric Relevance) model. The differences between the contributions of both MT_CKD models to near-infrared absorption and heating are modest for all three atmospheres. The additional absorption due the CAVIAR model more than doubles those due to both MT_CKD models for the tropical and mid-latitude summer atmospheres. For both atmospheres, the extra heating of the CAVIAR model is up to a factor of 5 more than those of the MT_CKD models. For the sub-arctic winter atmosphere, the differences between the extra absorption and heating of the CAVIAR and those of both MT_CKD models are relatively less. Thus, an update of the MT_CKD model from version 2.5 to 3.2 has a relatively small impact on near-infrared spectrally integrated absorbed solar fluxes and heating rates. But their contributions to the calculations of these quantities differ significantly from that of the much stronger CAVIAR model.
PubDate: 2021-02-12

• Calibration of Kain–Fritsch cumulus scheme in Weather Research and
Forecasting (WRF) model over Western Luzon, Philippines
• Abstract: Calibration of the cumulus parameterization scheme for localized areas is one method that can improve numerical weather prediction rainfall forecast accuracy. Calibration for model development, however, is a time-consuming procedure that requires numerous simulations. The utilization of an efficient method for calibrating complex dynamical models can help mitigate the heavy computational costs involved. In this study, five parameters in the Weather Research and Forecasting (WRF) Kain–Fritsch cumulus scheme were calibrated based on rainfall verification in the northwest and the southeast regions of the Philippines. Two optimization methods—Multiple Very Fast Simulated Annealing (MVFSA) and Adaptive Surrogate Modeling-Based Optimization (ASMO)—were used to find the best parameter set values. Both methods generated a higher coefficient of downdraft (Pd), lower entrainment (Pe), and longer convective available potential energy (CAPE) consumption time (Pc), which were found to result in better skill scores than the default WRF. Precipitation amount in both calibrated models decreased leading to an overall less wet bias. Precipitation skill score in the northwestern Philippines significantly improved by 35%, while that of the southeastern Philippines only increased by 3%. In addition, model calibration had no significant effect on the simulated temperature and wind speed. The results show that calibration of the cumulus parameterization scheme yields better results for convective rainfall rather than rain from stratiform clouds, which is expected since the cumulus parameterization scheme represents the effects of sub-grid-scale convective processes.
PubDate: 2021-02-10

• Budgets of rotational and divergent kinetic energy in the warm-sector
torrential rains over South China: a case study
• Abstract: The contributions of divergent and rotational wind components to the kinetic energy budget during a record-breaking rainstorm on 7 May 2017 over South China are examined. This warm-sector extreme precipitation caused historical maximum of 382.6 mm accumulated rainfall in 3 h over the Pearl River Delta (PRD) regions in South China. Results show that there was a high low-level southerly wind-speed tongue stretching into the PRD regions from the northeast of the South China Sea (SCS) during this extreme precipitation. The velocity potential exhibited a low-value center as well as a low-level divergence-center over the SCS. The rotational components of the kinetic energy (KR)-related terms were the main contribution-terms of the kinetic energy budget. The main contribution-terms of KR and the divergent component of kinetic energy (KD) were the barotropical and baroclinic processes-related terms due to cross-contour flow and the vertical flux divergence.
PubDate: 2021-02-05

• Variability of diurnal temperature range over Pacific Island countries, a
case study of Fiji
• Abstract: Diurnal temperature range (DTR) is an important index in climate change studies in addition to its influence upon environment and thermal comfort. Understanding variability in DTR at regional scales is, thus, important. In Fiji and other Pacific Island countries, DTR information is important in forecasting thermal comfort. This work is based on Fiji using gauge-based gridded mean monthly DTR data from the Climatic Research Unit (CRU). Annual and monthly DTR for the second half of the twentieth century to the present day are analyzed to establish temporal trends and spatial patterns. A combination of parametric and non-parametric tests was applied to investigate trend, correlation, simple linear regression, and interannual variability in the datasets. Findings show that DTR increases with an increase in latitude over Fiji. The mean monthly DTR between 6.58 and 7.37 °C in June and January coincide with winter and summer, respectively. Cumulative annual mean (CAM) of T, Tmx, and DTR showed increasing trends during the study period while the CAM of Tmn depicted a decreasing trend. Results suggest that DTR has a positive significant (insignificant) correlation with Tmx (T), but shows an opposing significant relationship with Tmn at 5% significance level. Each of DTR, Tmx, Tmn, and T experienced a general increase in values across the timeframe provided by the data. Records show an overall increase of 0.05 °C/decade in DTR. However, since the early 1990s, DTR has been characterized by a downward trend. Nonetheless, the overall trend of increasing DTR is explained by a greater increase in maximum temperatures over minimum temperatures. The observed rate of increase in DTR in warm months exceeds that in cold months. The findings form a baseline for further studies investigating the factors influencing DTR variability, and how the variability is affecting human thermal comfort.
PubDate: 2021-02-01

• Evaluation of global ensemble prediction models for forecasting medium to
heavy precipitations
• Abstract: In this study, 24-h forecasts of CMA, ECCC, ECMWF, KMA, NCEP and UKMO models were extracted from the TIGGE database and evaluated over selected stations in Iran within the 2010–2018 period. Daily forecast data were interpolated using the inverse distance method to the location of selected synoptic stations, while the frequency bias were further corrected using quantile mapping. The accumulation interval for precipitation was 24 h. Moreover, both raw and frequency-bias-corrected data were evaluated in deterministic and dichotomous modes at precipitation depth thresholds of 5, 15, 25, 35, and 45 mm. Forecasts were further partitioned and evaluated in different seasons in selected stations located in the margin of Alborz and Zagros Mountains. Best performing precipitation models did well over October–December season along the Alborz Mountains and over April–June along the Zagros Mountains. Results indicated that the forecasts were quite improved especially in high threshold after the frequency bias correction. Compared to other models, the ECMWF model provided best results in most stations. In contrast, NCEP performed poorly and could not provide a reliable medium to heavy precipitation forecasts. In general, none of the forecast models provided accurate estimates for precipitation depths over the 35-mm threshold. In fact, at higher precipitation thresholds, all models underpredicted the number of precipitation events.
PubDate: 2021-02-01

• Spatiotemporal characteristics of drought in a semi-arid grassland over
the past 56 years based on the Standardized Precipitation Index
• Abstract: Drought is one of the major natural disasters in northern China. Therefore, monitoring and analyzing the changes of drought indices can provide scientific evidence for disaster assessment and for instituting policies for disaster prevention and mitigation. In this work, we used the gridded precipitation data set of a semi-arid steppe region in the Inner Mongolian Plateau from 1962 to 2017 to calculated the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI). By comparing the spatiotemporal distribution of the two indices with the historical drought records, we found that the SPI was more suitable for the drought description in the study area. Based on this, we calculate the SPI at different time scales (annual and seasonal), then depict the spatiotemporal variation of drought during the past 56 years by analyzing the frequency of drought events, drought frequency, and the station frequency ratio. The results indicate that in recent years, the degree of drought in the study area has increased, and the south-central regions are the highest occurrence area for different types of drought events. In addition, light drought occurs mostly in the autumn and is mainly distributed in the central and southern parts of the study area. The most extreme drought events often occur in the summer, and the highest frequency area is located in the western part of the study area. Large-scale light drought occurred in all seasons in the 1990s, while the domain moderate drought and the domain extreme drought occurred in the summers of 2001 and 2010, respectively.
PubDate: 2021-02-01

• A review on the Indian summer monsoon rainfall, variability and its
association with ENSO and IOD
• Abstract: The Indian summer monsoon rainfall (ISMR) during June to September contributes most of the annual rainfall over India and plays an important role in Indian agriculture and thus the economy. It exhibits high spatio-temporal variabilities forced from both internal and external factors, which are important for better understanding and prediction of ISMR. Since the internal factors, mainly in the form of intraseasonal oscillations set a limit to the predictability, the major focus is given to the external forcing factors including the coupled air–sea interactions, sea surface temperature variations, snow cover, etc. This paper mainly aims to review the results of recent research analysis on ISMR variability and the major climate factors that determine the variability. Focus is given on the contributions from the coupled ocean–atmosphere processes in the Indian and Pacific Oceans to the ISMR variability [(primarily the El Niño Southern Oscillation (ENSO)] and Indian Ocean Dipole (IOD). Several studies were carried out in recent decades to explore the ISMR variabilities and their influences from tropical oceans. The studies, which focused the impact of ENSO and IOD on the ISMR variability have been considered in exploring their relationships and observed changes in recent decades. In the backdrop of varying relationship of ISMR with ENSO and IOD in the regional scale, it is important to study further the regional teleconnection of ISMR variabilities with oceanic factors, especially from the Indian and Pacific Ocean basin.
PubDate: 2021-02-01

• Changing characteristics and attribution analysis of potential
evapotranspiration in the Huang–Huai–Hai River Basin, China
• Abstract: Evapotranspiration is a key component of the hydrological cycle. It is important to understand the features of the variation of potential evapotranspiration and the impacts of its drivers to estimate regional water consumption. The Huang–Huai–Hai (HHH) River Basin is comprised of three major rivers (the Yellow, Huai and Hai) and has been threatened by water shortages and huge consumption of water for agricultural and industrial development. In this study of the Huang–Huai–Hai (HHH) River Basin, potential evapotranspiration (E 0) across the basin was calculated using the Penman–Monteith model, and their changing characteristics were detected by using the Mann–Kendall test. The test was based on the daily climatic variables from 1965 to 2014 at 175 meteorological gauges. In addition, the influential effect of net radiation (R n), relative humidity (RHU), wind speed (WIN), mean, maximum and minimum air temperature (T a, T max and T min) on E 0 were analyzed by using the climate elasticity method, with their relative contribution to the changes of E 0 quantitatively revealed by using the multiple linear regression method. The results showed that R n, WIN, RHU and T a are the predominant climatic predictors that are more influential to E 0 while T max and T min have the least impact. The increase in annual E 0 in the period of 1985–2014 in the HHH River Basin was mainly attributed to the significantly increasing T a, which may greatly offset the effect of decreasing WIN and R n. The decrease of annual E 0 in the period of 1965–2014 in the middle area of the basin was mainly attributed to the falling WIN and R n.
PubDate: 2021-02-01

• Mechanisms of an extreme fog and haze event in the megacities of central
and eastern China
• Abstract: In this study, the authors conduct a detailed analysis of a prolonged fog and haze (FAH) event in the megacities of central and eastern China in January 2013. The persistence and intensification mechanisms of high impact FAH weather are analyzed using surface and sounding data from national meteorological observation stations and national environmental pollutant observations. The large-scale circulation was characterized by the weak and stable polar vortex in Eurasia, a zonally elongated circulation spanning from the midlatitudes to high latitudes at 500 hPa, and the West Pacific Subtropical High extending further westward. At the surface, the FAH regions were under control by a weak easterly and southeasterly at the back or bottom of the surface high-pressure system, which was favorable for the accumulation of high aerosol pollutant concentrations. The formation and intensification of the FAH have an apparent correlation with the variation in meteorological variables and aerosols. The strengthening of the extreme fog was due to a combination of advection and radiation effects, among which alternative temperature advection played the key role in the maintenance and deterioration of FAH. The boundary layer height indicated the strengthening and weakening of the FAH. The persistence of weak vertical wind shear in the middle-lower layer and weak vertical motion were favorable dynamic factors for the FAH.
PubDate: 2021-02-01

• Structural time-series modelling for seasonal surface air temperature
patterns in India 1951–2016
• Abstract: To investigate variability patterns, to predict short-term and long-term changes of weather, time-series data analysis is considered to be a valuable tool. The paper presents modelling and forecasting the seasonal surface air temperature patterns in India for the period 1951–2016 using the structural time-series modelling. The structural time-series model (STSM) with the hidden components of deterministic linear time trend, trigonometric seasonal, and stochastic autoregression for cycle is selected from the parsimonious models. The model selection can be done based on Bayesian Information Criteria (BIC), significant tests, and statistical fit. The model parameters of the noise terms and the damping coefficient in the autoregression are determined using maximizing the likelihood function. The diagnostics of the selected STSM is determined with normal diagnostics checked by examining the histogram and Q–Q plot of residuals; the whiteness checked by autocorrelation function (ACF), partial autocorrelation function (PACF), and p values of LJung–Box portmanteau test of residuals. Furthermore, the forecast of seasonal surface air temperature patterns in India during the years 2017–2019 has been forecasted from the selected STSM. Statistically significant increase is noticed in annual average surface air temperature over India. The increase in surface temperature is about 0.0132 °C per year for the period 1951–2016. The study also showed slow and steady increase in the mean surface air temperature over India during the recent years.
PubDate: 2021-02-01

• Orographic and lake effect on extreme precipitation on the Iranian coast
of the Caspian sea: a case study
• Abstract: This paper presents the results of numerical modelling of extreme precipitation on the southern coast of the Caspian sea using the WRF-ARW model with realistic and idealised conditions aimed at evaluating the orographic and lake effects. Verified against the observational data, this model reproduces the spatial distribution and the total amount of precipitation in selected episodes reasonably well, with a certain set of physical parametrizations. Sensitivity tests showed that the lake effect is evident only in the presence of orography. The total contribution of the warm Caspian sea and orography to the amount of precipitation is, on average, 50%.
PubDate: 2021-02-01

• Comparison of mechanical and thermal effects of lake urmia: a case study
• Abstract: Simulation of the lakes’ impact on precipitation and the quantification of the effects resulting from the contribution of different processes have proved challenging in numerical weather prediction. Lakes have differences with their surrounding lands in roughness, surface temperature and humidity content, which are the root causes of the mechanical and thermal mechanisms that lead to changes in the amount of precipitation in the downwind of a lake. In this study, the effect of Lake Urmia on precipitation in the form of snowfall is simulated for a case of heavy precipitation associated with severe weather across a significant part of the Southwest Asia on 11 Dec. 2013 using the Weather Research and Forecasting (WRF) model coupled with the Community Land Model (CLM) in simulations with and without the lake. The results illustrate that omitting the lake reduces the humidity content and precipitable water in the vertical column above the lake. In this case, however, the role of mechanical effect due to differences in roughness between the lake and its surrounding lands is noticeably larger than the thermal effect due to differences in the moisture and heat supplied by the lower surface to the air masses above. Associated with the roughness differences, a distinct change in the location of the convergence line of the antithetical currents is identified over the lake. The case highlights the importance of the proper representation of surface characteristics in mesoscale models to gain improvements on the fine-scale details of precipitation.
PubDate: 2021-02-01

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