National Centers For Environmental Prediction Research Articles

An Analysis of Prediction Skill of Heat Waves Over India Using TIGGE Ensemble Forecasts

AbstractHeat wave has become a great concern for India in the recent years due to its disastrous impact on various sectors including health. Thus, accurate forecasts of heat wave events well in advance are required for preparing adequate mitigation strategies. The present study assesses the prediction skill of numerical weather prediction models in The Observing system Research and Predictability Experiment Interactive Grand Global Ensemble (TIGGE) experiments for predicting heat waves over India up to 7 days in advance. The models considered for this analysis are; the European Centre for Medium‐Range Weather Forecasts (ECMWF), the UK Met Office (UKMO) and National Centre for Environmental Prediction (NCEP). The model forecast verifications have been carried out for the hot weather season (April–June) over India for the period of 2008–2013. Fourteen heat wave events were identified during the study period using gridded daily maximum temperature (Tmax). The study reveals that the spatial distribution of maximum Temperature is well predicted by the TIGGE models for a forecast lead time of 1–7 days. The analysis suggested that the magnitude of heat wave events, even with a 7 days lead time, can be correctly predicted by more than 80% of ensemble members in all the TIGGE models. The prediction skill of the maximum temperatures over heat wave prone area during heat wave events is higher for ECMWF, then UKMO and NCEP models. The forecast verification analysis thus indicates that the TIGGE models are able to provide early warnings of heat waves with at least 5 days lead time.

Assimilation of Radio Occultation Data Using Measurement-Based Observation Error Specification: Preliminary Results

Abstract The local spectral width (LSW) of a radio occultation (RO) observation in impact parameter representation is a useful parameter for providing information on the uncertainty associated with the RO bending angle measurement. The LSW can potentially be used to specify the bending angle observation error (BaOE) in the lower troposphere for each individual sounding. This study assesses the usefulness and limitations of LSW in representing BaOE for a global data assimilation system. A two-step scheme is proposed to derive profile-dependent BaOE from LSW. Since the LSW-based BaOE varies with each individual RO observation, it is here designated as a dynamic BaOE (DBOE) in contrast to the traditional statistics-based BaOE specification. A benchmark control run and two sensitivity experiments are conducted with continuous cycling data assimilation using the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) and Global Forecast System (GFS). The usefulness and impact of the LSW-based DBOE are evaluated using radiosonde observations and global analyses. Results show that DBOE is able to improve the assimilation of RO data, leading to better forecast skill scores. Another experiment, in which the GSI statistical observation error of the benchmark run is replaced by the average of LSW-based DBOE, shows that the ability to assign larger weighting for high-quality observation and lower weighting for low-quality observation is the key factor for the success of the LSW-based DBOE.

Characterizing Warm Temperature during April 2019 Monsoon Transition in the Central part of Java

Warmer temperature than normal has been observed in the central part of Java Island-Indonesia during the monsoon transition in 2019. To explain the environmental process related to temperature variability, this research aims to characterize the process related to temperature anomaly and explain the contributing factors in terms of radiation balance and latent heat flux. This research uses the data from two meteorological stations from the Indonesian Bureau of Meteorology, Climatology, and Geophysics (BMKG) and the National Centers for Environmental Prediction (NCEP/NCAR) Reanalysis Project to explain the temperature anomaly. Several parameters, such as near-surface air temperature and humidity, cloudiness, solar and longwave radiation, as well as latent heat flux are obtained from the reanalysis data. This study focuses on descriptive time-series data analysis to explain the factors that contribute to the April 2019 temperatures. The results of this study show the effect of the Madden–Jullian Oscillation (MJO) on weather anomalies. The increase in humidity when the study area is traversed by the MJO causes an increase in humidity followed by intensive cloud formation and the release of latent heat in the cloud. The release of latent heat contributes significantly to the increase in temperature compared to the effect of the shortwave and longwave radiation balance. Thus, atmospheric phenomena in the tropics need to be studied further concerning warmer temperatures because they can be significantly affected by climate change.

Forecasting the Ensemble Hydrograph of the Reservoir Inflow based on Post-Processed TIGGE Precipitation Forecasts in a Coupled Atmospheric-Hydrological System

The quality of precipitation forecasting is critical for more accurate hydrological forecasts, especially flood forecasting. The use of numerical weather prediction (NWP) models has attracted much attention due to their impact on increasing the flood lead time. It is vital to post-process raw precipitation forecasts because of their significant bias when they feed hydrological models. In this research, ensemble precipitation forecasts (EPFs) of three NWP models (National Centers for Environmental Prediction (NCEP), United Kingdom Meteorological Office (UKMO) (Exeter, UK), and Korea Meteorological Administration (KMA) (SEOUL, REPUBLIC OF KOREA)) were investigated for six historical storms leading to heavy floods in the Dez basin, Iran. To post-process EPFs, the raw output of every single NWP model was corrected using regression models. Then, two proposed models, the Group Method of Data Handling (GMDH) deep learning model and the Weighted Average–Weighted Least Square Regression (WA-WLSR) model, were employed to construct a multi-model ensemble (MME) system. The ensemble reservoir inflow was simulated using the HBV hydrological model under the two modeling approaches involving deterministic forecasts (simulation using observed precipitation data as input) and ensemble forecasts (simulation using post-processed EPFs as input). The results demonstrated that both GMDH and WA-WLSR models had a positive impact on improving the forecast skill of the NWP models, but more accurate results were obtained by the WA-WLSR model. Ensemble forecasts outperformed coupled atmospheric–hydrological modeling in comparison with deterministic forecasts to simulate inflow hydrographs. Our proposed approach lends itself to quantifying uncertainty of ensemble forecasts in hydrometeorological the models, making it possible to have more reliable strategies for extreme-weather event management.

A deep learning approach for spatial error correction of numerical seasonal weather prediction simulation data

ABSTRACT Numerical Weather Prediction (NWP) simulations produce meteorological data in various spatial and temporal scales, depending on the application requirements. In the current study, a deep learning approach, based on convolutional autoencoders, is explored to effectively correct the error of the NWP simulation. An undercomplete convolutional autoencoder (CAE) is applied as part of the dynamic error correction of NWP data. This work is an attempt to improve the seasonal forecast (3–6 months ahead) data accuracy for Greece using a global reanalysis dataset (that incorporates observations, satellite imaging, etc.) of higher spatial resolution. More specifically, the publically available Meteo France Seasonal (Copernicus platform) and the National Centers for Environmental Prediction (NCEP) Final Analysis (FNL) (NOAA) datasets are utilized. In addition, external information is used as evidence transfer, concerning the time conditions (month, day, and season) and the simulation characteristics (initialization of simulation). It is found that convolutional autoencoders help to improve the resolution of the seasonal data and successfully reduce the error of the NWP data for 6-months ahead forecasting. Interestingly, the month evidence yields the best agreement indicating a seasonal dependence of the performance.

Assimilation of New York State Mesonet Surface and Profiler Data for the 21 June 2021 Convective Event

Abstract The New York State Mesonet (NYSM) has provided continuous in situ and remote sensing observations near the surface and within the lower troposphere since 2017. The dense observing network can capture the evolution of mesoscale motions with high temporal and spatial resolution. The objective of this study was to investigate whether the assimilation of NYSM observations into numerical weather prediction models could be beneficial for improving model analysis and short-term weather prediction. The study was conducted using a convective event that occurred in New York on 21 June 2021. A line of severe thunderstorms developed, decayed, and then reintensified as it propagated eastward across the state. Several data assimilation (DA) experiments were conducted to investigate the impact of NYSM data using the operational DA system Gridpoint Statistical Interpolation with rapid update cycles. The assimilated datasets included National Centers for Environmental Prediction Automated Data Processing global upper-air and surface observations, NYSM surface observations, Doppler lidar wind retrievals, and microwave radiometer (MWR) thermodynamic retrievals at NYSM profiler sites. In comparison with the control experiment that assimilated only conventional data, the timing and location of the convection reintensification was significantly improved by assimilating NYSM data, especially the Doppler lidar wind data. Our analysis indicated that the improvement could be attributed to improved simulation of the Mohawk–Hudson Convergence. We also found that the MWR DA resulted in degraded forecasts, likely due to large errors in the MWR temperature retrievals. Overall, this case study suggested the positive impact of assimilating NYSM surface and profiler data on forecasting summertime severe weather.

Water vapour multi-vortex structure under the interactions of typhoons and mid-low latitude systems during extreme precipitation in North China

With diverse atmospheric circulation and complex mechanisms, typhoon remote rainstorms (TRR) are closely related to a variety of mid-low latitude systems. The anomalous signatures in such processes can improve forecasting heavy rainfall associated with typhoons and fill the gaps in current physical conceptual models of TRR. Using the optimal typhoon path dataset, ground observation data, and the National Centres for Environmental Prediction (NCEP) reanalysis dataset, the impact of anomalous water vapour transport on excessive rainfall in North China is investigated. The vector empirical orthogonal function (VEOF) analysis is applied to typhoon precipitation events from 1970 to 2021, and three major modes are extracted to reflect the anomalous water vapour transport for typhoon precipitation. Mode 1 reflects a moisture circulation pattern in North China due to the coexistence of typhoons and remote and direct precipitation, with the highest probability of heavy precipitation. The differences in remote precipitation areas and intensities are attributed to the location of typhoons in the western North Pacific and the shifting of the subtropical high ridge. To characterise the water vapour transport circulation of TRR in North China, we proposed the ‘multi-vortex’ idea. The transmission of the multi-vortex ensures a continuous supply of TRR water vapour, and the enhancement of the multi-vortex is significantly linked to the enhancement of remote precipitation. Three extreme northern rainstorms, 75·8 Henan, 7·21 Beijing, and 7·20 Zhengzhou rainstorms, have anomalous multi-vortex water vapour convergence, similar to Mode 1. The mechanism of water vapour driven by multi-vortex in the three severe rainstorm events is more extreme than in usual TRR events in North China. The stronger Indian low vortex and relatively southerly subtropical highs can intensify the southwest branch of the double water vapour transport branch, whereas the strengthening of typhoons in the western North Pacific facilitates water vapour transport through the channel on the southwestern side of the subtropical highs and then transports it to key rainstorm areas, promoting TRR development. Therefore, the combination of subtropical highs, typhoons, and the anomalous multi-vortex structure may help in the establishment of key indicators of extreme precipitation in North China.

Development of statistical downscaling model based on Volterra series realization, principal components and ridge regression

AbstractImpacts of the global climate change in hydrology and water resources are accessed by downscaling of local daily rainfall from large-scale climate variables. This study developed a statistical downscaling model based on the Volterra series, principal components and ridge regression. This model is known, hereafter as SDCRR. The proposed model is applied at four different stations of the Manawatu River basin, in the North Island of New Zealand to downscale daily rainfall. The large-scale climate variables from the National Centers for Environmental Predictions (NCEP) reanalysis data are used in the present study to obtain with the wide range (WR) and the restricted range (RR) of predictors. The developed SDCRR model incorporated the climate change signals sufficiently by working with WR predictors. Further, principal component analysis (PC) was applied to the set of WR predictors, which were also used as the orthogonal filter in the ridge regression model to deal with the multi-collinearity. The ridge regression coefficients determined were less sensitive to random errors, and were capable of reducing the mean square error between the observed and the simulated daily precipitation data. Thus, the combined application of principal component analysis (PCA) and ridge regression improved the performance of the model. This combination is steady enough to capture appropriate information from predictors of the region. The performance of the SDCRR model is compared with that of the widely used statistical downscaling model (SDSM). The results of the study show the SDCRR model has better performance than the SDSM.

A comparison of East-Asia landfall tropical cyclone in recent reanalysis datasets--before and after satellite era

Based on different reanalysis datasets, reconstructions of East Asia landfall tropical cyclones (TCs) were compared with observations. The 20th-century reanalysis version 3 dataset (20CRv3) received the most approval in this assessment. It performed better in terms of annual frequency. The fifth generation of atmospheric reanalysis dataset (ERA5) and Japanese 55-year reanalysis dataset (JRA55) are also recommended in this study. Nevertheless, an apparent inconsistency in reconstructed TCs before and after 1980 is visible. Temporally, after the satellite era, the underestimation on TC frequency of the National Centres for Environmental Prediction and National Centre for Atmospheric Research (NCEP/NCAR) reanalysis dataset (NCAR) and 20-century reanalysis of European Center for Medium-Range Weather Forecasts (ERA20C) has been greatly improved. The downward trend of landfalling TCs is well captured by ERA5 and ERA20C. Spatially, the underestimation of TC track discrepancy is reduced in the post-satellite era. ERA5 and 20CRv3 showed relatively consistent performance compared to the former reanalysis in pre-and post-satellite time, which might be due to their better TC treatment. Despite the essential need for high resolution, this study stressed the importance of observation and assimilation development for the reanalysis TCs.

Evaluating western North Pacific tropical cyclone forecast in the subseasonal to seasonal prediction project database

The Daily Tropical Cyclone Probability (DTCP), defined as the probability of tropical cyclone occurrence within 500 km of a location in 1 day, is proposed and used in evaluating subseasonal to seasonal (S2S) predictions from the subseasonal to seasonal Prediction Project Database, from May 1 to October 31, 1999 to 2010. The ensemble forecasts are collected from eleven operational centers, the Bureau of Meteorology (BoM), China Meteorological Administration (CMA), Environment and Climate Change Canada (ECCC), European Centre for Medium-Range Weather Forecasts (ECMWF), Hydrometeorological Centre of Russia (HMCR), the Institute of Atmospheric Sciences and Climate of the National Research Council of Italy (ISAC), the Japan Meteorological Agency (JMA), the Korea Meteorological Administration (KMA), Météo-France/Centre National de Recherche Meteorologiques (METFR), the United States National Centers for Environmental Prediction (NCEP), and the United Kingdom Met Office (UKMO). In both observation and these eleven forecast models, the daily tropical cyclone probability is modulated by the Boreal Summer Intraseasonal Oscillation (BSISO), depicted by the two indices, boreal summer intraseasonal oscillation 1 and boreal summer intraseasonal oscillation 2. During boreal summer intraseasonal oscillation 1 phases 1, 5, 6, 7, and 8, the daily tropical cyclone probability in the western North Pacific region is ∼3.5 times higher. Similarly, during phases 1, 2, 3, 4, and 8 of boreal summer intraseasonal oscillation 2, the daily tropical cyclone probability is ∼2.5 times higher. Among the eleven models, the European Centre for Medium-Range Weather Forecasts model best reproduces the climatological daily tropical cyclone probability and its modulation by the boreal summer intraseasonal oscillation in the western North Pacific region, followed by the United States National Centers for Environmental Prediction, the Korea Meteorological Administration, the Japan Meteorological Agency models. Using the daily tropical cyclone probability metric, the highest debiased Brier Skill Score of the eleven models is from European Centre for Medium-Range Weather Forecasts, which has a slightly less skillful prediction than the reference climatological forecast with lead time 11–30 days. The skill of the eleven models is higher during the non-active phases of tropical cyclone activity than their skill during the active phases.

Investigation and analysis of the Iranian autumn rainfall thickness pattern

The purpose of this study was to investigate and analyze the trend of autumn precipitation thickness pattern in Iran. For this purpose, two environmental and atmospheric databases have been used. Environmental data is prepared and networked in two stages, in the first stage with the help of 1434 stations and in the second stage with the help of 1061 stations. Atmospheric data includes geopotential height data obtained from the National Center for Environmental Prediction and the National Center for Atmospheric Research (NCEP / NCAR). The spatial resolution of this data is 2.5 × 2.5 degrees. The thickness of the atmosphere, which is usually between 500 and 1000 hectopascals, is shown. This thickness is considered as the thickness of the whole atmosphere. The results of the autumn precipitation trend showed that although autumn precipitation on monthly and annual scales has experienced an increasing trend in most regions, in less than 5% of Iran, the upward trend has been significant. The most intense upward trend is observed in the form of spots in the central and northern parts of the Zagros Mountain, while the greatest decreasing trend has been observed in the form of cores along the Caspian coastal cities. The results of the autumn precipitation thickness pattern showed that the autumn precipitation thickness pattern is affected by deflection and instability due to high latitude cold and humid weather and low latitude hot and humid weather occurred in North Africa, in such a way that the Black Sea and the Mediterranean Sea provide the required moisture in high latitudes and the Red Sea and the Persian Gulf in low latitudes.

Using Neural Networks to Predict Hurricane Storm Surge and to Assess the Sensitivity of Surge to Storm Characteristics

AbstractHurricane storm surge represents a significant threat to coastal communities around the world. Here, we use artificial neural network (ANN) models to predict storm surge levels using hurricane characteristics along the US Gulf and East Coasts. The ANN models are trained with storm surge levels from a hydrodynamic model and physical characteristics of synthetic hurricanes which are downscaled from National Centers for Environmental Prediction (NCEP) reanalysis using a statistical‐deterministic hurricane model. The ANN models are able to accurately predict storm surge levels with root‐mean‐square errors (RMSE) below 0.2 m and correlation coefficients > 0.85. The ANN models trained with the NCEP data also show satisfactory accuracy (RMSE below 0.7 m; correlation > 0.7) in predicting storm surge levels for hurricanes downscaled from future climate projections. Once trained, we use the ANN models to assess the sensitivity of storm surge levels to variations in hurricane characteristics and local geophysical features. Progressively stronger maximum wind speeds and larger outer radius sizes independently increase storm surge levels at all locations along the US East and Gulf Coasts. The response of storm surge levels to changes in hurricane translation speed, however, is found to be sensitive to coastal configuration, with increases in hurricane translation speed amplifying (reducing) storm surge levels in open ocean (semi‐enclosed) regions.

Structures and Mechanisms of Heatwaves Related to Quasi-Biweekly Variability over Southern China

Abstract In the present study, the structures and mechanisms of the heatwaves (HWs) associated with the quasi-biweekly (QBW; 10–20-day period) variability (QBW-HW) over southern China (SC; 106°–120°E, 21°–30°N) are investigated by using observation data from surface stations in China and the related gridded dataset (CN05.1), and the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis. We found that the strongest anticyclonic anomaly and subsidence appear over SC during the developing phase of QBW-HW, and then induced excess solar radiation at surface and significant diabatic heating lead to a positive surface air temperature change, thus favoring occurrence of QBW-HW over SC. In addition, we found a wet near-surface atmosphere in the QBW-HW events over SC, and further confirmed that near-surface moisture should play an important role in the occurrence of QBW-HW, via absorptions of longwave and shortwave radiation. This result is quite different from previous studies since they did not pay attention to the near-surface moisture. On the other hand, warmer SAT favors more water vapor evaporated from the moist soil when considering the Clausius–Clapeyron relationship. Then, the positive feedback processes promote the occurrence of QBW-HW over SC. In contrast, during the developing and warm phases of QBW-HW over SC, except for the near-surface level, the troposphere is in a dry condition, even at 850 and 700 hPa. In the QBW-HW events over SC, the factor responsible for the wet near-surface atmosphere is the enhanced surface evaporation, which is attributed to strengthened surface wind speed and background moist soil. Significance Statement Under the background of global warming, heatwaves over Southern China are experiencing an increasing trend. In this study, we want to understand the structures and mechanisms of the heatwaves related to 10–20-day (quasi-biweekly) variability. We that found some structures of heatwaves (e.g., anticyclonic anomalies along with subsidence) are consistent with previous studies. In addition, we also show that the moist soil and increased induced near-surface moisture play a key role in the occurrence of heatwaves over Southern China, via enhanced absorptions of longwave and shortwave radiation. This study is helpful for understanding the processes and prediction of heatwaves over Southern China. Future work should examine the findings by some numerical experiments with a climate model.

Global Ocean Monitoring and Prediction at NOAA Climate Prediction Center: 15 Years of Operations

Abstract Climate variability on subseasonal to interannual time scales has significant impacts on our economy, society, and Earth’s environment. Predictability for these time scales is largely due to the influence of the slowly varying climate anomalies in the oceans. The importance of the global oceans in governing climate variability demonstrates the need to monitor and forecast the global oceans in addition to El Niño–Southern Oscillation in the tropical Pacific. To meet this need, the Climate Prediction Center (CPC) of the National Centers for Environmental Prediction (NCEP) initiated real-time global ocean monitoring and a monthly briefing in 2007. The monitoring covers observations as well as forecasts for each ocean basin. In this paper, we introduce the monitoring and forecast products. CPC’s efforts bridge the gap between the ocean observing system and the delivery of the analyzed products to the community. We also discuss the challenges involved in ocean monitoring and forecasting, as well as the future directions for these efforts.

Klerksdorp Tornado on March 4, 2007, in South Africa: A Synoptic Overview

This research delves into the factors contributing to tornado formation in South Africa, with a specific focus on the Klerksdorp tornado that occurred on March 4, 2007, in Northwest Province. Despite their recurrent occurrence and significant potential for damage, tornadoes have received relatively little attention. Between 1990 and 2014, these weather phenomena incurred estimated costs exceeding half a billion American dollars in addition to other weather-related disasters. In this study, data from multiple observation systems, including the National Oceanic and Atmospheric Administration (NOAA), the National Centers for Environmental Prediction (NCEP), the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) in Germany, and the South African Weather Service (SAWS), were thoroughly analyzed. The findings reveal that the Klerksdorp tornado was linked to a cold front and a cut-off low, which were the dominant weather systems on the day of the tornado. This case study enhances our comprehension of tornado dynamics in South Africa, aiding in improving short-term forecasts and potentially early warning systems. Future research should concentrate on the recurring nature of tornadoes in association with tropical weather systems and locally-driven factors.

On the verification of ensemble precipitation forecasts over the Godavari River basin

The ensemble forecasts from Numerical Weather Prediction (NWP) models are considered as essential elements for operational hydrologic forecasting. However, these forecasts are burdened with significant inherent biases and cannot be used directly for hydrologic forecasting. Therefore, the present study examines the capability of ensemble precipitation forecasts obtained from two NWP models i.e., a 20-member ensemble from the National Center for Environmental Prediction (NCEP) and a 50-member ensemble from the European center for Medium-term Weather Forecasts (ECMWF). The skill of 70-member multi-model grand ensemble (MME) generated from the NCEP and ECMWF forecasts is also verified in this study. Furthermore, post-processing of the raw ensemble members was carried out using two methods (Quantile Mapping (QM) and Quantile Regression Forests (QRF)) in order to minimize the inherent biases. The analysis was performed over subbasins of an Indian river basin namely the Godavari River Basin. Here, a set of deterministic and probabilistic verification measures (forecast error box plots, correlation coefficient, Relative Mean Error, mean Continuous Ranked Probability Score, spread-skill relationship, rank histograms, reliability diagram, Area under Relative Operating Characteristic Curve) were employed to assess various quality attributes of the forecasts. The findings from the study suggests that QRF post-processed NCEP and MME forecasts are comparatively better than other forecasts in terms of all employed verification measures over all subbasins. The QRF post-processed forecasts were found to be superior than QM post-processed and raw forecasts over all subbasins. The adopted post-processing methods resulted in improved forecasts at shorter lead times. However, the skill of both raw and post-processed forecasts declines at higher lead times over all subbasins.

Relative accuracy of HWRF reanalysis and a parametric wind model during the landfall of Hurricane Florence and the impacts on storm surge simulations

Prediction and reanalysis of storm surge rely on wind and pressure fields from either parametric tropical cyclone wind models or numerical weather model reanalysis, and both are subject to large errors during landfall. This study assesses two sets of wind/pressure fields for Hurricane Florence that made landfall along the Carolinas in September 2018 and appraises the impacts of differential structural errors in the two suites of modeled wind fields on the predictive accuracy of storm surge driven thereby. The first set was produced using Holland 2010 (H10), and the second set is the Hurricane Weather Research and Forecasting (HWRF) reanalysis created by the NWS National Centers for Environmental Prediction (NCEP). Each is validated using a large surface data set collected at public and commercial platforms and then is used as input forcing to a 2-D coastal hydrodynamic model (Delft3D Flexible Mesh) to produce storm surge along the Carolina coasts and major sounds. Major findings include the following. First, wind fields from HWRF are overall more accurate than those based on H10 for the periphery of the storm, though they exhibit limitations in resolving high wind speeds near the center. Second, applying H10 to the best track data for Florence yields an erroneously spike in wind speed on September 15th when the storm reduced to a tropical depression. Third, HWRF wind fields exhibit a progressively negative bias after landfall, likely due to deficiencies of the model in representing boundary layer processes, and to the lack of assimilation of surface product after landfall for compensating for these deficiencies. Fourth, using HWRF reanalysis as the forcings to Delft3D yields more accurate peak surges simulations, though there is severe underestimation of surge along the shoreline close to the track center. The peak surge simulations by Delft3D are biased low when driven by H10, even though over several locations the H10 model clearly overpredicts surface wind speeds. This contrast highlights the importance of resolving wind fields further away from the center in order to accurately reproduce storm surge and associated coastal flooding.

Mangroves in the “Plasticene”: High exposure of coastal mangroves to anthropogenic litter pollution along the Central-West coast of India

Anthropogenic litter is a ubiquitous stressor in the global ocean, and poses ominous threats to oceanic biodiversity and ecosystem functioning. At the terrestrial-ocean interface, tropical mangrove forests are subject to substantial exposure to mismanaged litter from inland and marine sources. While the effects of litter in different marine ecosystems are well-documented, research on the ecological consequences of litter pollution on mangroves remain nascent stage. Here, we investigated anthropogenic litter concentration, composition, probable sources, and impact on coastal mangroves along the Central West coast of India. The mean concentration of trapped litter was measured 8.5 ± 1.9 items/m2 (ranged 1.4 ̶ 26.9 items/m2), and 10.6 ± 0.5 items/tree (ranged 0 ̶ 85 items/tree) on the mangrove floor and mangrove canopy, respectively. Plastic dominated 83.02 % of all litter deposited on the mangrove forest floor and 93.4 % of all entangled litter on mangrove canopy. Most litter comprised single-use plastic products across all surveyed locations. Mangrove floor cleanliness was assessed using several indices, such as Clean Coast Index, General Index, Hazardous Items Index, and Pollution Load Index, reiterating an inferior cleanliness status. The pollution load index indicates “Hazard level I" plastic pollution risk across the mangroves. Litter concentration differed markedly across all sites. However, a significantly higher concentration of stranded litter was detected in the densely populated urban agglomeration and rural areas with inadequate solid waste management. Probable sources of litter indicate land-based (local) and sea-originated (fishing). Supportive information on the transport and accumulation of marine litter is examined based on the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) model version 2 reanalysis of surface wind and current pattern across the Arabian Sea followed by MIKE simulated tide-induced coastal current. Mangrove pneumatophores and branches were found to be damaged by entangled plastics. Hence, determining litter quantum and their probable input source is pivotal in mitigating anthropogenic litter impact on mangrove ecosystems and fostering mangrove conservation. Overall, results envisage that stringent enforcement, implementation of an integrated solid waste management framework, and general behavioral change of the public are crucial to mitigate litter/plastic pollution.

Intertropical Convergence Zone as the Possible Source Mechanism for Southward Propagating Medium-Scale Traveling Ionospheric Disturbances over South American Low-Latitude and Equatorial Region

This paper presents the Intertropical Convergence Zone (ITCZ) as the possible source mechanism of the medium-scale traveling ionospheric disturbances (MSTIDs) propagating to the southeast direction over the South American region. Using the data collected by the GNSS dual-frequency receivers network from January 2014 to December 2019, detrended TEC maps were generated to identify and characterize 144 MSTIDs propagating southeastward over the South American low-latitude and equatorial region. We also used images from the Geostationary Operational Environmental Satellite (GOES) 13 and 16 in the infrared (IR) and water vapor (WV) channel, and reanalisys data from the National Centers for Environmental Prediction (NCEP) of the National Oceanic and Atmospheric Administration (NOAA) to study the daily features and seasonal migration of ITCZ. In the winter, when ITCZ migrates to the northern hemisphere around 10–15° N, 20 MSTIDs propagated southeastward. During summer, when the ITCZ lies within the continent, around 0–5° S 80 MSTIDs were observed to propagate southeastward; in the equinoxes (spring and fall), 44 MSTIDs were observed. Again, the MSTIDs propagating southeastward showed a clear seasonality of their local time dependence; in summer, the MSTIDs occurred frequently in the evening hours, whereas those in winter occurred during the daytime. We also found for the first time that the day-to-day observation of ITCZ position and MSTIDs propagation directions were consistent. With regard to these new findings, we report that the MSTIDs propagating southeastward over the South American region are possibly induced by the atmospheric gravity waves, which are proposed as being generated by the ITCZ in the troposphere. The mean distribution of the horizontal wavelength, period, and phase velocity are 698 ± 124 km, 38 ± 8 min, and 299 ± 89 m s−1, respectively. For the first time, we were able to use MSTID propagation directions as a proxy to study the source region.

Variations of precipitation and runoff in the Bagmati river basin, Bihar, India

AbstractThe trend analysis of precipitation for four rain gauge stations and runoff for Hayaghat station in the Bagmati river basin is carried out in this work by adopting modified Mann–Kendall and Sen's slope methods. Primary and secondary data are used for finding the monthly, seasonal, and annual trends at four stations. Primary data are the observed rainfall from 1981 to 2013 which were collected from IMD Pune, observed runoff data were collected from CWC Patna and secondary data from the National Center for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis rainfall data for the period 1981–2013. The goals of this research are (i) to determine rainfall and runoff trend analysis, as well as the relationship between observed rainfall data and NCEP/NCAR reanalysis secondary data for four stations and (ii) to find the correlation between observed rainfall data and runoff data for all four seasons. The correlation analysis of observed rainfall and NCEP/NCAR reanalysis data shows a very good correlation ranging between 0.6111 and 0.7435. Rainfall is increasing during and after the monsoon at all selected stations, except during the monsoon season in Dhenge. Correlation analysis of rainfall and runoff shows ranges from 0.3724 to 0.4721 for all the four seasons. The correlation of rainfall and runoff is relatively good in monsoon season.HIGHLIGHTSTo determine rainfall and runoff trends, as well as the relationship between observed rainfall data and NCEP/NCAR reanalysis secondary data for four stations. To find the correlation between observed rainfall and runoff data for all four seasons.