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European Space Agency (ESA) launched its first space-based Doppler Wind Lidar (DWL) mission called Atmospheric Dynamic Mission (ADM) – Aeolus. Onboard the Aeolus mission is the Atmospheric LAser Doppler Instrument (ALADIN) which measures the horizontal line-of-sight (HLOS) winds. Aeolus Level-2B wind observations in Rayleigh clear and Mie cloudy channels are evaluated for implementation in the NCMRWF (National Centre for Medium Range Weather Forecasting) Global Forecast System (NGFS). The GSI (grid-point statistical interpolation) analysis scheme has been modified and updated to assimilate the HLOS wind information. Quality control criteria are applied during observation processing and during minimization of cost function for computation of the initial condition. An observation system experiment (OSE) is performed by employing the GSI-3DVar (3-Dimensional Variation) approach and involving HLOS data. In addition to assimilation and forecast diagnostics, two case studies of very severe cyclonic storms are investigated to assess the impact of this new wind information on a severe weather event. Statistically, significant improvement is observed mostly over the Southern Hemisphere, Tropics, and RSMC (Regional Specialized Meteorological Centre, 29°–120°E and 21°S–46°N) region in terms of reduction in wind root mean square error. Assimilation of HLOS winds shows a reduction in direct positional error (DPE) for both cyclonic systems. Improvement in the 6-hourly analysis of minimum sea level pressure and maximum 10 m wind speed is also observed. [ABSTRACT FROM AUTHOR]

In this study, a super cyclonic storm (Amphan) over Bay of Bengal was analyzed with the National Center for Medium-Range Weather Forecasting, Unified Model (NCUM) and National Center for Environmental Prediction (NCEP) global model data. Further, the weather research and forecasting (WRF) model has been utilized to simulate this super cyclonic storm with NCUM and NCEP data as initial and boundary conditions. The model is integrated, for every 12 h interval from 16 May 0000 UTC to 18 May 1200 UTC with horizontal resolution of 9 km and vertically at 34 levels. The results reveal that the track of the cyclone, central pressure of the cyclone, intensity of the cyclone, and landfall time of the cyclone are simulated by the model reasonably well in both the cases that are with NCUM-GFS and NCEP-GFS. However, the WRF simulations with NCEP data are closer to IMD's estimated track and intensity, when compared with the simulations with NCUM data. The relatively higher errors in simulations with NCUM could be due to the differences in the initial cyclone vortex against the observation in some initial conditions. [ABSTRACT FROM AUTHOR]

The ability to accurately predict the EI Nino-Southern oscillation (ENSO) is essential for seasonal climate forecasting. Monitoring the Pacific Ocean's surface temperature has many benefits for human life, including a better understanding of climate and weather, the ability to predict summer and winter, the ability to manage natural resources, serving as a reference for maritime transportation and navigation needs, serving as a reference for climate change monitoring needs, and even serving as a renewable energy source by utilizing high sea surface temperatures. This study introduces a deep learning (DL) model with AttentionSeq2Luong model as our proposed model to the ENSO research community. The present study showcases the capability of our proposed model to effectively forecast the forthcoming monthly average Nino index compared to the baseline seq2seq architecture model. For the dataset, this study utilized monthly observations of Nino 12, Nino 3, Nino 34, and Nino 4 between January 1870 and August 2022. The brief result of our experiment was that applying Luong Attention in the seq2seq model reduced the RMSE error by around 0.03494, 0.04635, 0.03853, and 0.03892 for forecasting Nino 12, Nino 3, Nino 34, and Nino 4, respectively. [ABSTRACT FROM AUTHOR]

Coastal upwelling plays a vital role in the support and maintenance of productive marine ecosystems throughout the California Current System (CCS). Here, we evaluate upwelling forecast skill using ∼30 years of seasonal reforecasts from four global climate models contributing to the North American Mulitmodel Ensemble (NMME). The models skillfully predict upwelling intensity throughout much of the CCS in boreal winter, and in the South‐Central CCS in spring/summer. The models also skillfully predict various aspects of upwelling phenology, including the timing of the spring transition, as well as the total vertical transport integrated over the course of the upwelling season. Climatic sources of forecast skill vary with season, with contributions from the El Niño‐Southern Oscillation in winter‐spring, and the North Pacific Oscillation and the North Pacific Meridional Mode in the winter‐summer. Our results highlight the potential of seasonal climate forecasts to inform management of upwelling‐sensitive marine resources. Plain Language Summary: Upwelling—the process of drawing cold, nutrient rich ocean waters toward the surface—plays a vital role in supporting vibrant and diverse biological populations throughout the California Current System (CCS). Here, we assess whether four global climate models can forecast seasonal upwelling 1–12 months in advance. We show that for much of the year (winter through summer), models can predict anomalous upwelling intensity several months in advance. We also show that the models can predict the timing of the "spring transition" (i.e., the start of the upwelling season) for much of the CCS. The model skill is related to large‐scale climate modes, including the El Niño‐Southern Oscillation, the North Pacific Oscillation, and the Pacific Meridional Mode. These climate modes alter the strength of the surface winds along the U.S. west coast from winter‐summer, giving rise to predictable patterns of upwelling. Our results highlight the potential of seasonal climate forecasts to inform management of upwelling‐sensitive marine resources. Key Points: Global climate models skillfully predict upwelling in the California Current System in winter‐summerModels also skillfully predict timing of spring transitionWinter skill is linked to the El Niño‐Southern Oscillation and North Pacific Oscillation, summer skill with the Pacific Meridional Mode [ABSTRACT FROM AUTHOR]

The global food production system is increasingly strained by abrupt and unpredictable weather events, which hinder communities' ability to adapt to climate variations. Despite advances in meteorological predictions, many communities lack the academic knowledge or infrastructure to interpret these complex models. This gap highlights the need for solutions that make climate forecasts more accessible and actionable, especially for communities reliant on natural resources. This study explores the potential of enhancing seasonal climate forecasts by integrating local ecological knowledge (LEK) with scientific data. Specifically, we combined ethnobiological information gathered between 2022 and 2024 with existing oceanographic and ecological data to create an ethnobiological calendar for four fishing cooperatives. An ethnographic approach was used to understand the population's ethnobiological knowledge and their perceptions of marine heatwaves and climate change impacts. Coastal monitoring data was collected using moorings that recorded temperature over a 14-year period (2010–2024). To characterize giant kelp dynamics, we used an existing dataset of multispectral Landsat images, which estimates the surface canopy biomass of giant kelp forests. Ecological monitoring was conducted annually every summer from 2006 to 2023 to record the in situ abundance of ecologically and economically important invertebrate and fish species. Combining oceanographic, ecological, and ethnographic data, allowed for alligning fishers' observations with recorded marine heatwave events and ecological shifts. Our findings revealed that these observations closely matched documented marine heatwave data and corresponding ecological changes. The integration of LEK with scientific oceanographic data can significantly improved our understanding of dynamic climate regimes, offering contextually relevant information that enhances the reliability and utility of seasonal climate forecasts. By incorporating yearly data into an ethnobiological calendar, we promote more inclusive, community-based approaches to environmental management, advocating for the integration of LEK in climate adaptation efforts, emphasizing its crucial role in strengthening resilience strategies against climatic shocks. [ABSTRACT FROM AUTHOR]

The Antarctic vortex of October–December 2020 was the strongest on record in the satellite era for the season in the mid‐ to lower stratosphere. However, it was poorly predicted by the Australian Bureau of Meteorology's operational seasonal climate forecast system of that time, ACCESS‐S1, even at a short lead time of a month. Using the current operational forecast system, ACCESS‐S2, we have, therefore, tried to find a primary cause of the limited predictability of this event and conducted forecast sensitivity experiments to understand the potential role of ozone in the event and its associated anomalies of the Southern Annular Mode (SAM) and rainfall over south–eastern Australia and western Patagonia. Here, we show that the 2020 strong vortex event did not follow the canonical dynamical evolution seen in previous strong vortex events in spring but suddenly appeared as a result of the record‐low upward propagating wave activity in September 2020. The ACCESS‐S2 forecasts significantly underestimated the negative wave forcing in September even at zero lead time, irrespective of the ozone configuration, therefore falling short in predicting the record strength of the polar vortex in late spring 2020. Nevertheless, ACCESS‐S2 with prescribed realistic ozone that had large anomalies in the Antarctic stratosphere significantly better predicted the strong vortex and the subsequent positive SAM and related rainfall anomalies over south–eastern Australia and western Patagonia in the austral summer of 2020–21. This highlights the potentially important role of ozone variations for seasonal climate forecasting as a source of long‐lead predictability. Plain Language Summary: The Antarctic vortex of October–December 2020 was the strongest on record in the satellite observation era for that season when monitored at 60°S in the mid‐ to lower stratosphere (altitudes of 15–30 km). However, this super vortex event was poorly predicted by the Australian Bureau of Meteorology (BoM)'s seasonal climate forecast system even at 1‐month lead time. We argue that the 2020 strong vortex was likely caused by an abrupt reduction in the upward propagating wave activity from the troposphere in September 2020, which left the stratospheric vortex undisturbed and strong. The BoM seasonal forecast system substantially underpredicted the negative wave forcing in September 2020, resulting in a poor forecast performance for the vortex strength in the following season. Forecast experiments prescribed with observed versus climatological ozone concentrations further show that using the observed ozone, characterized by a significant loss over Antarctica in spring 2020, improved the ensemble mean forecasts for the 2020 vortex strength by ∼10%–20% at different levels of the stratosphere and the associated surface climate features such as the poleward shift of the Southern Hemisphere midlatitude jet and anomalously high rainfall over south–eastern Australia and low rainfall over western Patagonia in the following summer. Key Points: The Antarctic vortex of 2020 was the strongest event in the satellite era for the October–December mean in the mid‐ to lower stratosphereSignificant lack of tropospheric wave forcing in September 2020 was responsible for the sudden appearance of the record strong polar vortexImposing realistic ozone concentrations significantly improved forecasts of the 2020 polar vortex strength and its downward impact [ABSTRACT FROM AUTHOR]

Ensemble Kalman Filters (EnKFs), which assimilate observations based on statistics derived from an ensemble of samples of ocean states, have become the norm for ocean data assimilation (DA) and forecasting. These schemes are commonly implemented with inflation and localization techniques to increase their ensemble spread and to filter out spurious long‐range correlations resulting from the limited‐size ensembles imposed by computational burden constraints. Such ad‐hoc methods were found to be not necessary in ensemble DA experiments with simplified ocean/atmospheric models and large ensembles. Here, we conduct a series of one‐year‐long ensemble experiments with a fully realistic EnKF‐DA system in the Red Sea using tens ‐to thousands of ensemble members. The system assimilates satellite and in‐situ observations and accounts for model uncertainties by integrating a 4‐km‐resolution ocean model with European Center for Medium Range Weather Forecast (ECMWF) atmospheric ensemble fields, perturbed internal physics and initial conditions for forecasting. OceanOur results indicate that accounting for model uncertainties is more beneficial than simply increasing the ensemble size, with the improvements due to large ensembles leveling off at about 250 members. Besides, and in contrast to what is commonly observed with simplified models, the investigated ensemble DA system still required localization even when implemented with thousands of members. These findings are explained by: (i) amplified spurious long‐range correlations produced by the low‐rank nature of the ECMWF atmospheric forcing ensemble; and (ii) non‐Gaussianity generated by the perturbed internal physical parameterization schemes. Large‐ensemble forcing fields and non‐Gaussian DA methods might be needed to get full benefits from large ensembles in ocean DA. [ABSTRACT FROM AUTHOR]

The paper justifies the possibility of improving the dynamical-statistical method of predicting monthly average surface air temperature through the application more advanced hydrodynamic models and statistical methods for operational use by the Hydrometeorological Center of Russia. A technology for monthly forecasting of surface air temperature anomalies is presented. The technology is based on using both the results of an improved 15-day medium-range weather element forecast scheme and the outcome of integrating the SL-AV model over a 16–30 day interval. Quality assessments of the forecasts obtained in real-time mode for 326 stations located in Russia are provided. The advantages of the proposed approach, particularly evident in cases of significant air temperature anomalies, are demonstrated. The obtained results are expected to be used in the technology for issuing long-range forecasts by the Hydrometeorological Center of Russia. [ABSTRACT FROM AUTHOR]

Seasonal rainfall forecasting is important for water resources management, agriculture, and disaster prevention. Our study aims to provide an automated deep learning method for the seasonal prediction of monthly rainfall at stations in seven climatic sub-regions in Vietnam with lead times of up to 6 months. An appropriate set of predictors was selected based on numerous climate indices and neighbor station data for the period 1980–2020. We developed an adapted deep learning pipeline for both short- and long-term analysis. The predicted rainfall was verified against the observed data using mean absolute error (MAE), root mean squared error (RMSE), and Pearson correlation coefficients. The results showed that our model generally captured well observed data reflected by low error (MAE and RMSE < 0.2) and high correlation (at 0.8–0.9) for all climatic sub-regions. For the leadtimes of 1–3 months, the rainfall predictionsmade using climate indices as predictors were outperformed by those using neighbor stations data; while for longer leadtimes (4–6 months), the climate indices themselve were able to improve the performance. The rainfall predictions of our methods on all three lead times climatological predictions by factoring additional values. However, there is room for improvement in predicting extreme and abrupt shifts in time series patterns. [ABSTRACT FROM AUTHOR]

Inadequate nitrogen (N)-fertilisation practices, that fail to consider seasonally variable weather conditions and their impacts on crop yield potential and N-requirements, cause reduced crop N-use efficiency. As a result, both the ecological and economic sustainability of crop production systems are put at risk. The aim of this study was to develop a season-specific crop forecasting approach that allows for a targeted application of N in winter wheat while maintaining farm revenue compared to empirical N-fertilisation practices. The crop forecasts of this study were generated using the process-based crop model SSM in combination with state-of-the-art seasonal ensemble weather forecasts (SEAS5) for the case study region of Eastern Austria. Results from three winter wheat on-farm experiments showed a significant reduction in applied N when implementing a crop forecast-based N-application approach (-43.33 kgN ha-1, -23.42%) compared to empirical N-application approaches, without compromising revenue from high-quality grain sales. The benefit of this reduced N-application approach was quantified through the economic return to applied N (ERAN). While maintaining revenue, the lower amounts of applied N led to significant benefits of + 30.22% (+ 2.20 € kgN-1) in ERAN. [ABSTRACT FROM AUTHOR]

Copyright of Plateau Meteorology is the property of Plateau Meteorology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Operational forecasting of solar radiation is critical for better decisionmaking by solar energy system operators, due to the variability of energy resources and demand. Although the numerical weather forecasting (NWP) model can predict solar radiation variables, there are often significant errors, especially in direct normal irradiation (DNI), which are influenced by the type and concentration of aerosols and clouds. This paper presents an artificial neural network (ANN) based method to generate operational DNI forecasts using weather and aerosol forecast data from the European Center for medium-range weather forecasts (ECMWF) and Copernicus atmospheric monitoring service (CAMS) respectively. The ANN model is designed to predict weather and aerosol variables at a certain time as input, while other models use the DNI forecast improvement period before the instant forecast. The model was developed using North Sumatra location observations and obtained DNI forecasting results every 10 minutes on the first day with DNI forecasting compared to the initial forecasting which was scaled down with the R², mean absolute error (MAE), and relative mean square error (RMSE) models were 0.6753, 151.2, and 210.2 W/m², so that and provides good agreement with experimental data. [ABSTRACT FROM AUTHOR]

Weather forecasting is crucial for scientific research and society. Recently, deep learning (DL) methods have achieved significant advancements in medium-range weather forecasting. However, they generally depend on the initial fields generated by the computationally expensive four-dimensional variational (4DVar) data assimilation (DA) technique, which limits their real-time applicability in multivariate three-dimensional (3D) weather forecasting. Here we propose 4DVarFormer by exploring the potential of integrating the 4DVar constraint into an attention-based neural network. 4DVarFormer eliminates the need for background error covariance statistics and the complex adjoint model development. It can generate multivariate 3D weather states within 0.37 s. Moreover, 4DVarFormer can capture inter-variable relationships, allowing the assimilation of observed variables to correct unobserved variables. Hence, medium-range forecasts initiated by 4DVarFormer outperform those of DL-based DA methods and achieve performance comparable to the forecasts initiated by ERA5 reanalyses. These promising findings contribute to future advancements in integrated end-to-end DL weather forecasting systems. [ABSTRACT FROM AUTHOR]

The applicability and accuracy of high‐resolution Real‐Time Meso‐scale Analysis (RTMA) system is assessed over India for the first time. The RTMA is a high‐spatial (2.5 km) and temporal resolution analysis system for near‐surface weather conditions. It is used to simulate near‐surface air temperature over India during the Heatwave (HW) period 12th to 20th April 2023. The verification analysis of temperature using the GLDAS gridded temperature shows reasonable improvement in the analysis from RTMA by capturing regional features compared to first‐guess. The spatial and temporal verification using the IMD station observations also confirms the value addition of RTMA in capturing the locations of recorded highest Tmax and their daily variations. The location‐specific heat stress analysis of RTMA shows high skill over many locations during HW days. Heat stress regions have been accurately brought out in the RTMA. Hence, the RTMA can be used for now‐casting and severe weather monitoring. Plain Language Summary: Heat wave (HW) alert systems require accurate high‐resolution surface weather data for better adaption strategies. Real‐Time Meso‐scale Analysis (RTMA) system configured to generate surface weather parameters at a high‐spatial (2.5 km) and temporal (3 hrs) resolution at National Center for Medium Range Weather Forecasting (NCMRWF) for the very first time in India. We verified its performance in representing the HW conditions and extreme temperatures over the Indian region relative to India Meteorological Department (IMD) observations/Global Land Data Assimilation System (GLDAS) during the HW period of 12th to 20th April 2023. Our results indicate significant improvement in the spatial and temporal variations of daily maximum temperature simulated by RTMA system. We have noted lower absolute mean errors of ∼ 0.02 for the RTMA in predicting the Tmax over the HW affected regions of Bihar, Odisha, West Bengal, and East Uttar Pradesh when compared to the GFS model. These improvements have led to the better identification of the locations of higher heat risk through the computation of the Heat Index and highlight the applicability of RTMA. Overall this study clearly brings out the usability of RTMA final analysis fields for now‐casting and severe weather monitoring. Key Points: It is a first‐of‐its‐kind study to understand the applicability and accuracy of high‐resolution RTMA over India by analyzing heatwaveRTMA temperature analysis shows higher skill over the heatwave regions and locations as compared to the background fields and observationsThe study suggested that the RTMA final analysis fields are highly useable for severe weather monitoring and impact‐based weather forecasts [ABSTRACT FROM AUTHOR]

Multiple record-breaking rainfall events were observed along the Western Ghats (WG) during the recent monsoon seasons (2019–2021). Rainfall amounts of up to > 200 mm/day (Extreme rainfall, ER) were recorded especially over the Mumbai region (19.07 N, 72.8 E) causing flooding, landslides, damage to infrastructure and loss of life. Thus, to enhance the resilience of this region by providing early warning for flooding, the National Center for Medium-Range Weather Forecasting Unified model's regional forecasting system (NCUM-reg) provides rainfall forecasts up to 3 days (72-h), which are utilized in the integrated flood warning system hydrological model. This study focuses on evaluating the performance of NCUM-reg forecasts during ER events. For this purpose, we have systematically performed verification of regional model operational forecasts using the suite of observations (rain gauge, satellite) and newly generated NCMRWF's regional reanalysis, Indian Monsoon Data Assimilation and Analysis (IMDAA). Key findings indicate that NCUM-reg model with explicit convection is performing well in representing the synoptic and dynamic features of the ER events similar to those observed. Quantitative assessment of the forecasts shows the strength of in-situ observations. In addition, the results summarize the importance of continuous and quality-controlled observations and stress the need for collective efforts of observations and new verification metrics (like process-oriented diagnostics) to enhance our understanding and as well as the model's ability in forecasting such events. [ABSTRACT FROM AUTHOR]

Due to low adaptive capacities and high reliance on weather-sensitive natural ecosystems for their livelihoods and food production, smallholder farmers are highly vulnerable to risks associated with climate change and variability. Such risks are location-specific; thus, to cope with them, smallholder farmers require tailored adaptation strategies. To support these farmers, this study aimed to identify climatic risks threatening sustainable smallholder farming in the Limpopo Province regions of Gavaza, Ga-Makanye and Giyani. Weather station records spanning up to 1980–2021 were used to identify climatic risks, for the October–April dryland maize growing season. Risks were identified based on temporal trends and/or interannual variability patterns for reference evapotranspiration and a suite of agriculturally relevant rainfall and temperature indices. Based on the identified risks, tailor-made adaptation strategies were devised for application by farmers within the study region. The results revealed the specific risks of growing seasons becoming shorter and increasingly hotter with highly irregular rainfall patterns. To adapt and improve agricultural productivity despite these prevailing climatic risks, farmers within the study region will benefit if they plant drought tolerant, early maturing and higher yielding crop varieties, apply soil water conservation techniques and make use of seasonal and daily weather forecasts to guide their decision-making. [ABSTRACT FROM AUTHOR]

Europe and the Mediterranean are considered climate change hot spots. This is the reason why this paper focuses on the analysis of the trends of essential climate variables in a Mediterranean country, Greece. The analyzed period is 1991–2020, and the dataset used is ERA5-Land (produced by the European Center for Medium-Range Weather Forecasts), which has global coverage and an improved resolution of ~9 × 9 km compared to other datasets. Significant climatic changes across Greece have been put in evidence during the analyzed period. More specifically, the country averaged a 30-year trend of temperature of +1.5 °C, locally exceeding +2 °C, and this increasing trend is positively correlated with the distance of the areas from the coasts. Accordingly, the number of frost days has decreased throughout the country. In terms of rainfall, a major part of Greece has experienced increasing annual rainfall amounts, while 86% of the Greek area has experienced a positive trend of days with heavy rainfall (>20 mm). Finally, a multiple signal of the trend of consecutive dry days was found (statistically non-significant in the major part of Greece). [ABSTRACT FROM AUTHOR]

This paper analyzes the ability of three conceptual stochastic models (one-box, two-box, and diffusion models) to reproduce essential features of sea surface temperature variability on intra-annual time scales. The variability of sea surface temperature, which is particularly influenced by feedback mechanisms in ocean surface–atmosphere coupling processes, is characterized by power spectral density, commonly used to analyze the response of dynamical systems to random forcing. The models are aimed at studying local effects of ocean–atmosphere interactions. Comparing observed and theoretical power spectra shows that in dynamically inactive ocean regions (e.g., north-eastern part of the Pacific Ocean), sea surface temperature variability can be described by linear stochastic models such as one-box and two-box models. In regions of the world ocean (e.g., north-western Pacific Ocean, subtropics of the North Atlantic, the Southern Ocean), in which the observed sea surface temperature spectra on the intra-annual time scales do not obey the ν − 2 law (where ν is a regular frequency), the formation mechanisms of sea surface anomalies are mainly determined by ocean circulation rather than by local ocean–atmosphere interactions. The diffusion model can be used for simulating sea surface temperature anomalies in such areas of the global ocean. The models examined are not able to reproduce the variability of sea surface temperature over the entire frequency range for two primary reasons; first, because the object of study, the ocean surface mixed layer, changes during the year, and second, due to the difference in the physics of processes involved at different time scales. [ABSTRACT FROM AUTHOR]

We evaluate the skill and sources of skill in initialized seasonal climate forecasts of monthly global mean temperature from the North American Multi‐Model Ensemble (NMME) during the period 1991–2024. The forecasts demonstrate skill in addition to that from the long‐term trend, and that skill is primarily attributable to ENSO. However, the skill varies seasonally, with skill being lowest for target periods during Northern Hemisphere summer. Single model ensembles show underdispersion at short leads, while the multi‐model ensemble is overdispersed, suggesting initial condition errors and highlighting the importance of model initialization for quantification of forecast uncertainty. Lead‐time dependent errors in global mean temperature trends appear related to Pacific trend errors. The multi‐model mean captured the overall trend but underestimated the record‐breaking temperatures of 2023. Forecasts for the remainder of 2024 indicate cooling by the end of the year. Plain Language Summary: Our study looked at how well current climate forecast models can predict the global average temperature up to a year in advance. We found that these models can predict temperature changes better than just looking at long‐term trends, and that their ability to do so was related to the climate phenomenon called ENSO (El Niño‐Southern Oscillation). However, the accuracy of these predictions depends on the time of year, being less accurate when predicting temperatures during the Northern Hemisphere's summer months. We also found that individual models often have too narrow a range of predictions, while combining multiple models results in a range that is too wide. Despite these deficiencies, the combined model predictions generally followed the observed temperatures but failed to predict the extreme high temperatures of 2023. Looking ahead, the models suggest a cooling trend by the end of 2024. This research improves our understanding of what current climate forecast models can tell us about global mean temperature in the short term and highlights areas where improvement is needed. Key Points: Initialized seasonal climate forecasts of global mean temperature show skill beyond the trend and that skill is largely related to ENSOForecast skill is lowest for target months during northern hemisphere summer due to forecast amplitudes that are too largeAt short leads single model ensembles are underdispersed and the multi‐model ensemble is overdispersed, which suggests initialization errors [ABSTRACT FROM AUTHOR]

In this study, wind speed sensitivity and calibration bias were first determined for Surface Water and Ocean Topography (SWOT) satellite Ka-band Radar Interferometer (KaRIn) Normalized Radar Backscatter Cross Section (NRCS) data at VV and HH polarizations. Here, the calibration bias was estimated by comparing the KaRIn NRCS with collocated simulations from a model developed using Global Precipitation Measurement (GPM) satellite Dual-frequency Precipitation Radar (DPR) data. To recalibrate the bias, the correlation coefficient between the KaRIn data and the simulations was estimated, and the data with the corresponding top 10% correlation coefficients were used to estimate the recalibration coefficients. After recalibration, a Ka-band NRCS model was developed from the KaRIn data to retrieve ocean surface wind speeds. Finally, wind speed retrievals were evaluated using the collocated European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis winds, Haiyang-2C scatterometer (HY2C-SCAT) winds and National Data Buoy Center (NDBC) and Tropical Atmosphere Ocean (TAO) buoy winds. Evaluation results show that the Root Mean Square Error (RMSE) at both polarizations is less than 1.52 m/s, 1.34 m/s and 1.57 m/s, respectively, when compared to ECMWF, HY2C-SCAT and buoy collocated winds. Moreover, both the bias and RMSE were constant with the incidence angles and polarizations. This indicates that the winds from the SWOT KaRIn data are capable of correcting the sea state bias for sea surface height products. [ABSTRACT FROM AUTHOR]

The atmospheric circulation significantly influences the snowpack over mid-high-latitude Eurasia. This study examines the characteristics of the leading subseasonal variability mode of boreal winter sea level pressure (SLP) with 20-80-day period and its relationship with snowpack over mid-high-latitude Eurasia, using the fifth generation of European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) data and different snowpack datasets. The SLP leading mode, characterized by a monopole pattern with a strong surface anomalous high centered near the Ural Mountains, exhibits a barotropic structure and extends from the surface to the tropopause. Above SLP and geopotential height anomalies propagate southeastward from the Barents-Kara Sea to East Asia. This leading SLP mode contributes to surface air temperature (SAT) and snowfall circulation anomalies over mid-high-latitude Eurasia. The latter two both directly influence on snowpack anomalies in situ. Over high latitude region, snowfall circulation anomaly is the dominant factor to control the snow depth anomaly. Over middle latitude region, both SAT and snowfall circulation anomalies lead to the snowpack anomaly. Furthermore, the response of snow depth to the leading subseaonal SLP mode occurs 2–5 days earlier than the response of snow cover to the same mode over middle latitude region. In addition, it is suggested that the Arctic Oscillation (AO), East Atlantic/West Russia (EAWR) and Polar/Eurasia (PEU) pattern may contribute to the development of the leading SLP mode and subsequently influence snowpack anomalies. [ABSTRACT FROM AUTHOR]

Downscaling seasonal climate forecasts using regional climate models (RCMs) became an emerging area during the last decade owing to RCMs' more comprehensive representation of the important physical processes at a finer resolution. However, it is crucial to test RCMs for the most appropriate model setup for a particular purpose over a given region through numerical experiments. Thus, this sensitivity study was aimed at identifying an optimum configuration in the Weather, Research, and Forecasting (WRF) model over Ethiopia. A total of 35 WRF simulations with different combinations of parameterization schemes for cumulus (CU), planetary boundary layer (PBL), cloud microphysics (MP), longwave (LW), and shortwave (SW) radiation were tested during the summer (June to August, JJA) season of 2002. The WRF simulations used a two-domain configuration with a 12 km nested domain covering Ethiopia. The initial and boundary forcing data for WRF were from the Climate Forecast System Reanalysis (CFSR). The simulations were compared with station and gridded observations to evaluate their ability to reproduce different aspects of JJA rainfall. An objective ranking method using an aggregate score of several statistics was used to select the best-performing model configuration. The JJA rainfall was found to be most sensitive to the choice of cumulus parameterization and least sensitive to cloud microphysics. All the simulations captured the spatial distribution of JJA rainfall with the pattern correlation coefficient (PCC) ranging from 0.89 to 0.94. However, all the simulations overestimated the JJA rainfall amount and the number of rainy days. Out of the 35 simulations, one that used the Grell CU, ACM2 PBL, LIN MP, RRTM LW, and Dudhia SW schemes performed the best in reproducing the amount and spatio-temporal distribution of JJA rainfall and was selected for downscaling the CFSv2 operational forecast. [ABSTRACT FROM AUTHOR]

Copyright of Plateau Meteorology is the property of Plateau Meteorology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We investigate why the North American Multi‐Model Ensemble (NMME) upper‐level height forecast for December–February (DJF) 2023/24 differs from the expected El Niño response. These atypical height anomalies emerged despite the fact a strong El Niño was forecast. The analysis focuses on diagnosing the NMME forecasts of DJF 2023/24 for SSTs and 200‐hPa heights initialized at the beginning of November 2023 relative to other ensemble mean NMME DJF forecasts dating back to 1982. The results demonstrate that forecasts of the 200‐hPa height anomalies had a large contribution from warming trends in global SSTs. It is the combination of trends and the expected El Niño teleconnection that results in the forecast height anomalies. Increasingly, for forecasts of geopotential height anomalies during the recent El Niño winters, the amplitude of trends is nearly equal to the signal from El Niño and has implications for the climatological base period selection for seasonal forecasts. Plain Language Summary: Seasonal forecasts are cast as anomalies as users want to know what can be expected beyond the typical seasonal swings of the climate. This necessitates a choice for the climatological base period relative to which forecast anomalies are computed. It, however, poses a challenge under rapid climate change. In this scenario, climate trends become part of the real‐time forecast anomalies, and if the climatological base period is sufficiently different, may even start to dominate. This was the case for the NMME DJF 2023/24 forecast of 200‐hPa heights which was forecast to be a strong El Niño, and yet, forecast for 200‐hPa heights differed from typical El Niño signal. The analysis implies that seasonal forecasts for some variables, consideration of trends is important and reliance on expected signal from El Niño—Southern Oscillation alone may not be sufficient. Key Points: The North American Multi‐Model Ensemble (NMME) seasonal forecasts for December–February (DJF) 2023/24 upper‐level height differ from the expected El Niño signalIt is the combination of trends in heights and the expected El Niño signal that results in the forecast NMME ensemble mean heights anomaliesThe forecast of trends is increasingly important to account for NMME forecast anomaly and their amplitude in recent years can be of same magnitude as the signal from El Niño [ABSTRACT FROM AUTHOR]

The article discusses the development and implementation of a real-time flood forecasting and spatial decision support system for Chennai, India. The system aims to mitigate the extensive damage caused by floods in the region. It utilizes ensemble rainfall forecasts and integrates meteorological, hydrological, and hydraulic models to provide accurate flood predictions. The system has been piloted and operationalized for the Northeast Monsoon seasons of 2021, 2022, and 2023, and has shown promising results in terms of forecast accuracy. The system also includes modules for short-term weather forecasting, inflow forecasting to reservoirs, lake operational guidance, hydraulic modeling, real-time data acquisition, and web-based decision support. [Extracted from the article]

A long-range forecast system based on the improved version of the SLAV072L96 global atmosphere model has been verified at the Hydrometcenter of Russia. The model has the horizontal resolution of 0.9° 0.72° in longitude and latitude and 96 vertical levels and includes modern parametrizations for the subgrid-scale processes in the atmosphere and active soil layer. Main features and particularities of this model version are presented along with a brief description of ensemble long-range meteorological forecast technology using this model. Some verification scores for long-range forecasts based on the archive data from ERA5 reanalysis for 1991–2015 and on the data for 2023 are presented. [ABSTRACT FROM AUTHOR]

Using the fifth generation European Center for Medium-range Weather Forecasts (ERA5) reanalysis data, we present a detailed examination of the climatological features of convectively coupled Kelvin waves (CCKWs) over the Indian and Pacific basins. The composited horizontal structure of Indian CCKWs resembles the theoretical Kelvin waves, with a maximum wave response at the equator. In contrast, the Pacific counterpart exhibits a very different pattern, characterized by a significant northward shift of the convective center, along with enhanced meridional winds and a relatively stronger wave response. The moist static energy (MSE) budget analysis is conducted to elucidate the physical factors that control the energetics of CCKWs. Despite the marked contrast in horizontal structure between Pacific and Indian CCKWs, the energy cycle and the physical factors that maintain this cycle are rather similar. During the recharge period (days -2 and -1), the column process (including vertical MSE advection, apparent heat source and moisture sink) functions to destabilize the atmosphere by importing the MSE; while the horizontal MSE advection tends to destabilize the atmosphere on day -2 but starts to stabilize the atmosphere earlier on day -1. During the discharge and transition period (from days 0 to + 2), the column process functions to stabilize the atmosphere by exporting the MSE; while the horizontal MSE advection inclines to stabilize the atmosphere on days 0 and + 1 but again starts to destabilize the atmosphere earlier on day + 2. The leading of horizontal MSE advection to the recharge-discharge cycle clearly points out the importance of the former in driving the eastward propagation of CCKWs. Both the horizontal MSE advection and column process are vital in maintaining the energy cycle of CCKWs, as they often take turns leading the role in recharging and discharging the atmosphere. [ABSTRACT FROM AUTHOR]

While there is huge demand for regional forecasts, information needed for selection of the most appropriate temporal and spatial scales is not available. The objective of this study is to demonstrate the basis of forecasting monthly mean rainfall over homogeneous regions by analyzing the forecasting skill and source of predictability. Reforecasts generated at the National Center for Medium Range Weather Forecasting (NCMRWF) for the period 1993–2015 using the coupled Unified Model are used in this study. Analysis of the forecasting skill over increasingly large lead times, averaging periods and spatial scales, is carried out to compare the skill at different time‐scales and to highlight the effect of spatial averaging over regions of coherent rainfall characteristics. Analysis of probabilistic forecasts is carried out to further demonstrate the usefulness of monthly mean forecasts. The influence of forcings on rainfall is studied both in model and in observations to understand the model's skill in representing interannual variability of monthly mean rainfall. Multiple regression analyses carried out for rainfall using climate indices as independent variables shows that the extent of forcings can largely explain the high variability of rainfall during the onset and withdrawal phase compared to the peak phase of monsoons. ENSO‐related subsidence is found to influence mainly the southern peninsular region, while tropical sea surface temperatures (SSTs) in the Indian Ocean are found to influence rainfall over northwest and central India by forcing circulation patterns typically associated with circumglobal teleconnections (CGTs) which are strongest during the month of June. Interestingly, the influence of CGTs on rainfall in the northeast is opposite to its influence on other homogeneous regions, which explains the contrast in influence of the North Indian Ocean SSTs on rainfall over the northeast and over All India. The model representation of influence of forcings and strength of teleconnections is better for specific region–month pairs, which is seen to influence the monthly variations in skill of forecasting rainfall over homogeneous regions. [ABSTRACT FROM AUTHOR]

Information regarding the uncertainty associated with weather forecasts, particularly when they are related to a localized area at convective scales, can certainly play a crucial role in enhancing decision-making. In this study, we discuss and evaluate a short-range forecast (0–75 h) from of a regional ensemble prediction system (NEPS-R) running operationally at the National Centre for Medium Range Weather Forecasting (NCMRWF). NEPS-R operates at a convective scale (~ 4 km) with 11 perturbed ensemble members and a control run. We assess the performance of the NEPS-R in comparison to its coarser-resolution global counterpart (NEPS-G), which is also operational. NEPS-R relies on initial and boundary conditions provided by NEPS-G. The NEPS-G produces valuable forecast products and is capable of predicting weather patterns and events at a spatial resolution of 12 km. The objective of this study is to investigate areas where NEPS-R forecasts could add value to the short-range forecasts of NEPS-G. Verification is conducted for the period from 1st August to 30th September 2019, covering the summer monsoon over a domain encompassing India and its neighboring regions, using the same ensemble size (11 members). In addition to standard verification metrics, fraction skill scores, and potential economic values are used as the evaluation measures for the ensemble prediction systems (EPSs). Near-surface variables such as precipitation and zonal wind at 850 hPa (U850) are considered in this study. The results suggest that, in some cases, such as extreme precipitation, there is a benefit in using regional EPS forecast. State-of-the-art probabilistic measures indicate that the regional EPS has reduced under-dispersion in the case of precipitation compared to the global EPS. The global EPS tends to provide higher skill scores for U850 forecasts, whereas the regional EPS outperforms the global EPS for heavy precipitation events (> 65 mm/day). There are instances when the regional EPS can provide a useful forecast for cases, including moderate rainfall, and can add more value to the global EPS forecast products. The investigation of diurnal variations in precipitation forecasts reveals that although both models struggle to predict the correct timing, the time phase and peaks in precipitation in the convection-permitting regional model are closer to the observations. [ABSTRACT FROM AUTHOR]

this paper proposes an optimized Quantum Key Distribution (QKD) protocol using entanglement swapping techniques to extend transmission range and improve error correction. Additionally, integrates an advanced error correction technique which is Low Density Parity Check (LDPC) and multi-hop quantum repeaters for more enhancement of the protocol performance. Hybrid Quantum Classical Error Correction Methods is applied ensuring compatibility and optimal performance and to manage the increased complexity. Simulations prove that 25% improvement in transmission distance with entanglement swapping. 50% improvement with advanced error correction and a 100% improvement with multi-hop quantum repeaters compared to existing protocols. These discoveries are supported by both theoretical analysis and simulation results, indicating significant decreases in error rates and extensions in maximum transmission distances. Comparative analysis made with existing protocols and that demonstrated the superiority of proposed approach in terms of extended secure communication distance, higher key generation rate and improved resilience to attacks. [ABSTRACT FROM AUTHOR]

Atmospheric gravity waves (GWs) impact the circulation and variability of the atmosphere. Sub‐grid scale GWs, which are too small to be resolved, are parameterized in weather and climate models. However, some models are now available at resolutions at which these waves become resolved and it is important to test whether these models do this correctly. In this study, a GW resolving run of the European Center for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS), run with a 1.4 km average grid spacing (TCo7999 resolution), is compared to observations from the Atmospheric Infrared Sounder (AIRS) instrument, on NASA's Aqua satellite, to test how well the model resolves GWs that AIRS can observe. In this analysis, nighttime data are used from the first 10 days of November 2018 over part of Asia and surrounding regions. The IFS run is resampled with AIRS's observational filter using two different methods for comparison. The ECMWF ERA5 reanalysis is also resampled as AIRS, to allow for comparison of how the high resolution IFS run resolves GWs compared to a lower resolution model that uses GW drag parametrizations. Wave properties are found in AIRS and the resampled models using a multi‐dimensional S‐Transform method. Orographic GWs can be seen in similar locations at similar times in all three data sets. However, wave amplitudes and momentum fluxes in the resampled IFS run are found to be significantly lower than in the observations. This could be a result of horizontal and vertical wavelengths in the IFS run being underestimated. Plain Language Summary: Small‐scale atmospheric waves, known as gravity waves (GWs), transport energy and momentum and affect the dynamics of the atmosphere. GWs in a high resolution run of the European Center for Medium‐Range Weather Forecasts Integrated Forecasting System (IFS) weather model are compared to those in observations from the Atmospheric Infrared Sounder (AIRS) instrument on NASA's Aqua satellite, to test how well these waves are resolved in the model. Nighttime data are compared over part of Asia and surrounding regions, during the first 10 days of November 2018. Since the high resolution IFS run has a higher vertical resolution, and a significantly higher horizontal resolution than the satellite observations, the model is resampled as if the satellite was viewing the model atmosphere. This removes GWs with horizontal and vertical wavelengths outside of the ranges that can be seen in the observations, allowing the data sets to be compared. Gravity waves formed by wind flowing over mountain ranges can be seen at similar times and in similar locations in the IFS run and observations, but wave amplitudes in the resampled IFS run are found to be significantly lower. Key Points: A kilometer‐scale Integrated Forecasting System (IFS) run is resampled as Atmospheric Infrared Sounder (AIRS) using two different methods to allow for comparison of gravity wave (GW) propertiesGWs can be seen in the resampled IFS run and AIRS at similar times and locationsMean amplitudes in the resampled IFS run are found to be significantly lower than in the observations by a factor of ∼2.77 [ABSTRACT FROM AUTHOR]

The West African Monsoon (WAM) system plays a crucial role in the West African climate system because it transports moisture from the Atlantic Ocean into the subcontinent in summer. This study evaluates the capability of the Model for Prediction Across Scales-Atmosphere (MPAS-A) to simulate the characteristic reproduce the WAM system and the associated rainfall-producing features. The MPAS model was used to perform a 30-year global climate simulation (1981–2010) at a regular grid (uniform resolution of 60 km). The simulation was initialized with the Climate Forecast System Reanalysis (CSFR) dataset. The results showed that MPAS simulate well the rainfall pattern over West Africa and reproduces the different phases of the monsoon dynamics system (i.e., the northward progression, the peak period, and the southward retreat). The model also reasonably replicates the pattern of the zonal components of wind and the vertical velocity. However, MPAS underestimates the orographic rainfall over the Guinea Coast, Jos Plateau, and Mount Cameroon. It also underestimates the vertical velocity and zonal wind magnitudes over the region. In addition, the model features a weaker temperature gradient than in the reanalysis. Understanding and correcting the sources of these model biases will enhance the suitability of MPAS for weather and seasonal forecasts over West Africa. Research highlights: In this study, we investigated the ability of the Model for Prediction Across Scales (MPAS) to simulate the West African monsoon rainfall, the associated atmospheric features controlling the variability of the seasonal and annual cycle, and the thermal wind conditions. First, the model is initialized using the CSFR reanalysis. Then, we run the model simulation for 30 years, from 1981 to 2010, using a regular grid (uniform resolution of 60 km). We showed that the model realistically simulated the spatial pattern of rainfall over West Africa. However, it has some problems when it comes to capturing the monsoon features. [ABSTRACT FROM AUTHOR]

In this study, we evaluate the soil moisture and precipitation products obtained from two reanalysis models: the National Centers for Environment Prediction (NCEP) and the European Center for Medium-Range Weather Forecasts (ECMWF). The study centers on Kuwait's semi-arid region, during the wet season (November to May) from 2008 to 2018. For the precipitation-related evaluation dataset, rain gauge records from the Kuwait Automatic Weather Observation System (KAWOS) were used, while the ground-truth soil moisture values were taken from the Climate Change Initiative (CCI-SM). Initially, to ensure CCI-SM reliability, we compare it with in-situ soil sensor measurements deployed at a desert site. The analysis revealed a maximum CCI-SM overestimation in winter, decreasing progressively throughout the year with 20% mean bias. The bias-corrected CCI-SM dataset is used for the comprehensive evaluation of the soil moisture reanalysis data. Accuracy metrics, such as mean bias (MB), correlation coefficient (R), and unbiased Root Mean Square Difference (ubRMSD), were used for this purpose. The results indicate that ERA5 consistently underestimates (~ 50% MB) soil moisture, but responds well under high soil moisture conditions. NCEP mostly overestimates soil moisture by a similar magnitude, providing even twice as high values during spring months. Mean monthly precipitation (MP) is also overestimated by NCEP, particularly during extreme episodes, yet found to be reliable enough regarding annual accumulated precipitation. ERA5 has shown strong (R ~ 0.6–0.9) predictive capabilities under both frontal and convective precipitation conditions, with ~ 3% median bias for MP, making it a promising alternative data source, particularly in regions with limited weather station coverage. [ABSTRACT FROM AUTHOR]

In this study, a Long Short-Term Memory (LSTM) network approach is employed to evaluate the prediction performance of PM2.5 in the Beijing–Tianjin–Hebei region (BTH). The proposed method is evaluated using the hourly air quality datasets from the China National Environmental Monitoring Center, European Center for Medium-range Weather Forecasts ERA5 (ECMWF-ERA5), and Multi-resolution Emission Inventory for China (MEIC) for the years 2016 and 2017. The predicted PM2.5 concentrations demonstrate a strong correlation with the observed values (R2 = 0.871–0.940) in the air quality dataset. Furthermore, the model exhibited the best performance in situations of heavy pollution (PM2.5 > 150 μg/m3) and during the winter season, with respective R2 values of 0.689 and 0.915. In addition, the influence of ECMWF-ERA5's hourly meteorological factors was assessed, and the results revealed regional heterogeneity on a large scale. Further evaluation was conducted by analyzing the chemical components of the MEIC inventory on the prediction performance. We concluded that the same temporal profile may not be suitable for addressing emission inventories in a large area with a deep learning method. [ABSTRACT FROM AUTHOR]

Severe heat waves lasting for weeks and expanding over hundreds of kilometres in horizontal scale have many harmful impacts on health, ecosystems, societies, and economy. Under the ongoing climate change heat waves are becoming even longer and hotter, and as proactive adaptation, the development of early warning services is essential. Weather forecasts in extended range (2 weeks to 1 month) tend to indicate a higher skill in predicting warm extremes than average temperature events in Europe. We verified hindcasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) in forecasting heat wave days, i.e., periods with the 5-day mean temperature being above its 90th percentile. The verification was done in 5° × 2° resolution over Europe, based on the forecast week (1 to 4 weeks). In the first forecast week, it is evident that across Europe, the accuracy of ECMWF heat wave forecasts surpasses that of a mere climatological forecast. Even into the second week, in many places in Europe, the ECMWF forecasts prove to be more reliable than their statistical counterparts. However, if we extend the forecast lead time to 3–4 weeks, predictability begins to lower to such a level that it can no longer be said, with the exception of Southeastern Europe, that the forecasts in general were statistically significantly better than the statistical forecast. Nonetheless, intense and prolonged heat waves during the third forecast weeks appear to have a higher-than-average level of predictability. [ABSTRACT FROM AUTHOR]

This study establishes an evaluation index system for residents' happiness and empirically examines the impact of traffic exhaust pollution (TEP) on residents' happiness using data from the China General Social Survey (CGSS), Statistical Yearbook, and European Center for Medium-Range Weather Forecasts (ECMWF). The findings demonstrate that TEP significantly influences residents' happiness: a 1 unit increase in TEP leads to a 0.105 unit increase in life satisfaction, a 0.233 unit decrease in emotional experience, a 0.109 unit decrease in social adaptation, and a 0.088 unit decrease in overall happiness. Mediation analysis reveals two pathways of influence: economic prosperity resulting from TEP enhances residents' happiness, while travel intention diminishes it. Heterogeneity analysis indicates variations across time windows, population groups, and regions. The impact of TEP on life satisfaction becomes evident after six months, with the effect on emotional experience remaining statistically significant for up to nine months. Social adaptation and overall happiness continue to be significantly affected for 15 months, while the impact on self-actualization persists even longer. Vulnerable groups including middle-aged individuals, seniors, women, rural residents, and those residing in economically developed areas are more prone to experiencing negative emotions and dissatisfaction with life due to TEP exposure. Furthermore regression analyses reveal that household income levels, public budge, travel intention play mediating roles between TEP exposure and happiness outcomes. These findings significantly contribute to the comprehension of the influence of TEP on residents' happiness, with the objective of fostering the high-quality advancement of environmental happiness initiatives and enhancing residents' overall welfare. [ABSTRACT FROM AUTHOR]

Forecasting uncertainties among meteorological fields have long been recognized as the main limitation on the accuracy and predictability of air quality forecasts. However, the particular impact of meteorological forecasting uncertainties on air quality forecasts specific to different seasons is still not well known. In this study, a series of forecasts with different forecast lead times for January, April, July, and October of 2018 are conducted over the Beijing-Tianjin-Hebei (BTH) region and the impacts of meteorological forecasting uncertainties on surface PM2.5 concentration forecasts with each lead time are investigated. With increased lead time, the forecasted PM2.5 concentrations significantly change and demonstrate obvious seasonal variations. In general, the forecasting uncertainties in monthly mean surface PM2.5 concentrations in the BTH region due to lead time are the largest (80%) in spring, followed by autumn (~50%), summer (~40%), and winter (20%). In winter, the forecasting uncertainties in total surface PM2.5 mass due to lead time are mainly due to the uncertainties in PBL heights and hence the PBL mixing of anthropogenic primary particles. In spring, the forecasting uncertainties are mainly from the impacts of lead time on lower-tropospheric northwesterly winds, thereby further enhancing the condensation production of anthropogenic secondary particles by the long-range transport of natural dust. In summer, the forecasting uncertainties result mainly from the decrease in dry and wet deposition rates, which are associated with the reduction of near-surface wind speed and precipitation rate. In autumn, the forecasting uncertainties arise mainly from the change in the transport of remote natural dust and anthropogenic particles, which is associated with changes in the large-scale circulation. [ABSTRACT FROM AUTHOR]

Seasonal precipitation forecasting is vital for weather-sensitive sectors. Global Circulation Models (GCM) routinely produce ensemble Seasonal Climate Forecasts (SCFs) but suffer from issues like low forecast resolution and skills. To address these issues in this study, we introduce a post-processing method, Quantile Ensemble Bayesian Model Averaging (QEBMA). It utilises quantiles from a GCM ensemble forecast to create a pseudo-ensemble forecast. Through their reasonable linear relationships with observations, each pseudo-member connects a hurdle distribution with a point mass at zero for dry months and a gamma distribution for wet months. These distributions are mixed to construct a forecast probability distribution with their weights, proportional to the quantiles' historical forecast performance. QEBMA is applied to three GCMs, including GloSea5 from the United Kingdom, ECMWF from Europe and ACCESS-S1 from Australia, for monthly precipitation forecasts in 32 locations across four climate zones in Australia. Leave-one-month-out cross-validation results illustrate that QEBMA enhances forecast skills compared to raw GCMs and other post-processing techniques, including quantile mapping and Extended Copula Post-Processing (ECPP), for forecast lead time of 0 to 2 months, based on five metrics. The skill improvements achieved by QEBMA are often statistically significant, particularly when compared to raw GCM forecasts across the 32 study locations. Among these post-processing models, only QEBMA consistently outperforms the SCF benchmark climatology, offering a promising alternative for improving seasonal precipitation forecasts. [ABSTRACT FROM AUTHOR]

The current paper evaluates the weather research and forecasting (WRF) model sensitivity to five different combinations of cumulus, microphysics, radiation, and planetary boundary layer (PBL) schemes over Loess Plateau for the period 2015, in terms of 2 m temperature and precipitation. The WRF configuration consists of a 10 km resolution domain nested in a coarser domain driven by European Center for Medium-Range Weather Forecasts Reanalysis (ERA-Interim) data. The model simulated 2 m temperature and precipitation have been evaluated at daily and monthly scales with gridded observational dataset. The analysis shows that all experiments reproduce well the daily 2 m temperature, with overestimation particularly in the low-temperature range. Precipitation is less well simulated, with underestimation in all range, especially for intense rainfall. Comparing with ERA-Interim, WRF shows no clear benefit in simulating daily 2 m temperature while prominent improvement in simulating daily precipitation. WRF simulations capture the annual cycle of monthly 2 m temperature and precipitation with a warm bias and wet bias for most experiments in summer. Some reasonable configurations are identified. The "best" configuration depends on the criteria. [ABSTRACT FROM AUTHOR]

This study evaluates the decadal prediction skill of 13 forecast systems in predicting winter precipitation over Eurasia, contributing to the Decadal Climate Prediction Project of the Coupled Model Intercomparison Project Phase 6. Northeast Asia stands out as a region with improved decadal prediction skill for forecast years 2–5 due to the initialization. Observations show anticyclonic and cyclonic wind anomalies over the North Pacific and Northeast Asia, respectively, with southwesterly flow to the east of Northeast Asia. Ten forecast systems reproduce such circulation anomalies favoring abundant winter precipitation in Northeast Asia. The significant positive (negative) correlations between the detrended Northeast Asian precipitation (NEAP) and AMV (PDO-like) time series are reproduced by seven (nine) forecast systems. However, most forecast systems underestimate the correlation between the NEAP and the AMV, and have relatively low skill in predicting the PDO. Further improvements in these aspects will help to improve the decadal prediction skill of winter precipitation over Northeast Asia. The multi-model ensemble (MME) is able to reproduce both links of NEAP with AMV and PDO-like variability. The MME demonstrates significant skill and outperforms the individual forecast systems in predicting the NEAP for all 4-year averaged periods in the range of 1–8 years, demonstrating the benefits of using the ensemble mean of multiple models. [ABSTRACT FROM AUTHOR]

The Madden–Julian Oscillation (MJO) provides an important source of global subseasonal-to-seasonal (S2S) predictability, while its prediction remains great challenges. Based on an atmosphere–ocean coupled model and the widely-used nudging method, suitable initialization and ensemble schemes are explored toward an improved MJO prediction. Results show that the ensemble strategy with perturbed atmospheric nudging coefficients facilitates adequate ensemble spread and hence improves the prediction skill, particularly for weak cases. Finally, an 18-member ensemble subseasonal prediction system called NUIST CFS1.1 is developed. Skill evaluation indicates that the NUIST CFS1.1 can extend the MJO prediction to 24 days lead, which reaches the world-average level but is far from the estimated potential predictability (~ 45 days). The limited skill at longer lead times corresponds to forecast errors exhibiting slower propagation and weaker intensity, which are jointly caused by the initial errors and model's imperfections. The latter is associated with unrealistic representations of MJO-related physical processes. The model underestimates the diabatic heating (mostly contributed by the latent heat release) of enhanced convection and fails to reproduce the suppressed convection within the MJO structure, collaboratively weakening the Kelvin/Rossby waves. This causes weaker horizontal winds and ultimately reduces the horizontal moisture advection on the two flanks of MJO convection. Furthermore, the underestimated Kelvin wave induces insufficient planetary boundary layer (PBL) convergence and thereby results in poor simulation of PBL premoistening ahead of MJO convection. Interestingly, the commonly dry bias is absent in our model, and the bias in horizontal moisture gradients may have a minor impact on the MJO propagation. Further efforts are urged to improve the model physics involving cumulus and PBL processes. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) over the Bay of Bengal (BOB) can interact with the South Branch Trough (SBT) as they move northward and potentially amplify Rossby waves. This study evaluates the features of Rossby waves and their downstream impact on rainfall in South China. Results indicate that TC‐SBT interactions primarily occur in May and October‐November (Oct‐Nov), with probabilities of 59% and 53% respectively. Notably, the Rossby wave train associated with BOB TCs is more pronounced during Oct‐Nov due to the stronger subtropical westerly jet, in contrast to May. The downstream atmospheric response results in positive (negative) rainfall anomalies over South China in May (Oct‐Nov), particularly on the day following the maximum interaction day. Previous researches concerning TC‐extratropical flow interaction mainly focus on other basins where TCs move to higher latitudes, this study provides fresh insights into Rossby waves related to TC‐SBT interactions over the southern Tibetan Plateau. Plain Language Summary: During bimodal periods of tropical cyclone (TC) activity over the Bay of Bengal (BOB) in May and October‐November (Oct‐Nov), the Asian subtropical westerly jet is usually located at lower latitudes over the southern Tibetan Plateau, where South Branch Trough (SBT) is active. We quantified the extent of BOB TC‐SBT interaction based on the negative potential vorticity advection by TC‐associated irrotational wind. The mean location of the TC center and maximum interaction point are around (20°N, 88°E) and (33°N, 89°E), respectively. Rossby wave trains (RWT) associated with BOB TCs may extend eastward to 150°W, dispersing at a faster zonal group speed in May compared to Oct‐Nov. In May, the downstream response in South China featured by amplified upper‐level divergent outflow, intensified mid‐level warm and cold air convergence, and enhanced low‐level southwesterly water vapor transport, which was conducive to precipitation. In Oct‐Nov, the RWT shifted northward and the anticyclone anomaly related to the subtropical high dominated South China, leading to a northward shift in water vapor transport, unfavorable for precipitation in South China. The results are significant for the short‐ to medium‐range weather forecasts in downstream regions of BOB TCs. Key Points: Tropical cyclones (TC) over the Bay of Bengal (BOB) interacting with the South Branch Trough (SBT) can amplify mid‐latitude Rossby wavesThe Rossby wave associated with interactions between BOB TCs and SBTs is stronger in October‐November than in MayThe atmospheric response results in positive (negative) precipitation anomalies over South China in May (October‐November) [ABSTRACT FROM AUTHOR]

In recent years, there has been growing evidence that latent heat release in midlatitude weather systems such as warm conveyor belts (WCBs) contributes significantly to the onset and maintenance of blocking anticyclones (blocked weather regimes). Still, numerical weather prediction (NWP) and climate models struggle to correctly predict and represent atmospheric blocking in particular over Europe. Here, we elucidate the representation of WCB activity in 20 years of extended winter (1997–2017) of European Centre for Medium‐Range Weather Forecast's IFS reforecasts around the onset of blocking over Europe (EuBL) employing different perspectives. First, we show that the model struggles to predict EuBL onsets already at 10–14 days lead time in line with a misrepresentation of WCB activity in the ensemble mean. However, we also find cases with accurate EuBL forecasts even in pentad 4 (15–19 days). This subset of successful forecasts at extended‐range lead times goes in line with accurate WCB forecasts over the North Atlantic several days prior to the blocking onset. Second, investigating the time‐lagged relationship of blocking onset and WCB activity, we find that WCB activity over the North Atlantic emerges well prior to the onset of the block and that different pathways into EuBL exist in the reforecasts compared to reanalysis. Finally, we find indication of predictability associated with a Rossby wave train emerging from the North Pacific. Although our study can not disentangle the roles of intrinsic predictability limits and model deficiencies, we show that correct predictions of EuBL go along with distinct patterns of WCB activity. Plain Language Summary: Warm conveyor belts (WCBs) are weather systems associated with low pressure systems which occur predominantly over the ocean regions of the midlatitudes. Several recent studies highlight the role of latent heat release due to cloud formation in WCBs for the development of long‐lived high pressure systems. However, current weather prediction and climate models struggle to accurately predict these high pressure systems, particularly over Europe (EuBL). This study, based on 20 years (1997–2017) of forecast data, reveals challenges in predicting WCB activity together with the onset of EuBL, especially within a lead time of 10–14 days. Successful predictions at extended lead times (15–19 days) align with precise forecasts of WCB activity over the North Atlantic and North Pacific, indicating a connection between these regions. Examining the timing of the onset of EuBL and WCB activity, we show that WCB activity over the North Atlantic precedes EuBL. Additionally, we identify both correct and incorrect pathways leading to EuBL and suggest that predictability of EuBL may arise from specific atmospheric patterns, particularly related to Rossby waves in the North Pacific. Key Points: Warm conveyor belt (WCB) activity around the onset of atmospheric blocking over Europe (EuBL) is analyzed in reanalysis and sub‐seasonal reforecastsCorrect WCB prediction provides a sub‐seasonal window of forecast opportunity for EuBL onsetSynoptic activity over the North Pacific supports the development of a teleconnection that affects EuBL onset [ABSTRACT FROM AUTHOR]

Artificial intelligence is one of the most popular, frequently discussed, and, meanwhile, ambiguous and controversial metaphorical concepts, which defines a scientific direction in computer science that studies the techniques for gaining knowledge, their computer representation, transformation, and application. Presently, it is intensively penetrating into many areas of human activities, including hydrometeorological ones. The concept of artificial intelligence, the history of its origin, and its methods and technologies are considered. The author analyzes the studies related to the use of artificial intelligence in short- and medium-range weather forecasting, including the collection and quality control of meteorological information, assimilation of data in order to generate initial conditions for numerical weather prediction models, development of forecast models and parameterization schemes for physical processes, postprocessing and physical-statistical interpretation of the output data of numerical weather prediction models. [ABSTRACT FROM AUTHOR]

Temperature and water vapor play crucial roles in the Earth's climate system, and it is important to understand and monitor the variation in the thermodynamic profile within the lower troposphere. Among various observation platforms for understanding the vertical structure of temperature and humidity, ground-based Fourier-transform infrared (FTIR) can provide detailed information about the lower troposphere by complementing the limitations of radiosonde or satellite methods. However, these ground-based systems have limitations in terms of cost, operation, and mobility. Herein, we introduce a cost-effective and easily deployable FTIR observation system designed to enhance monitoring capabilities for atmospheric conditions. The atmospheric downwelling radiance spectrum of sky is measured by applying a real-time radiative calibration using a blackbody. From the observed radiance spectrum, the thermodynamic profile (temperature and the water vapor mixing ratio) of the lower troposphere was retrieved using an algorithm based on the optimal estimation method (OEM). The retrieved vertical structure results in the lower troposphere were similar to the fifth-generation reanalysis database (ERA-5) of the European Center for Medium-range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction final analysis (NCEP FNL). This provides a potential possibility for monitoring atmospheric conditions by a compact FTIR system. [ABSTRACT FROM AUTHOR]

Spatiotemporal variations in temperature (T) and equivalent temperature (Te) significantly impact agricultural production across Pakistan, highlighting the need for enhanced weather and climate modeling. This study utilized four reanalysis datasets spanning a 38-year period (1981–2018): the fifth-generation European Center for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5), Interim ECMWF reanalysis (ERA-Interim), Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA2), and the Japanese 55-year reanalysis (JRA55). We employed National Oceanic and Atmospheric Administration/Advanced Very High-Resolution Radiometer (NOAA/AVHRR) Normalized Difference Vegetation Index (NDVI) data, a proxy for crop health, to assess the relationship between T, Te, and NDVI. This relationship is examined via regression and correlation analyses, and significance is assessed using the Mann–Kendall test and t-test. Our results show that near-surface T significantly contributes to the magnitude of Te (> 90%), whereas specific humidity (SH) has a smaller impact (< 10%). Both T and Te increase significantly across the entire tropospheric column, at 0.15 – 0.31 and 0.38 – 0.77 °C/decade, respectively. Notably, the mid-tropospheric level exhibits less warming than the upper and lower tropospheric levels. Correlation analyses of T and Te with NDVI reveal that Te exhibits a significantly stronger relationship with NDVI compared to T on both seasonal and annual timescales. The highest correlation occurs in the warm and humid summer monsoon (June – August), with Te showing a correlation of 0.50 and T correlating at 0.22 with NDVI. This study suggests that Te can serve as an additional metric for analysing near-surface heating trends in relation to crop health. [ABSTRACT FROM AUTHOR]

Background: Snow forms when tiny ice crystals in clouds stick together to become snowflakes. If enough crystals stick together, they become heavy enough to fall to the ground. Where background includes Precipitation falls as snow when the air temperature is below 2 °C (275.15 K). The falling snow does begin to melt as soon as the temperature rises above freezing, but as the melting process begins, the air around the snowflake is cold. It is a myth that it needs to be below 0 °C (273.15) K to snow. Objective: In Iraq, the heaviest snowfalls tend to occur when the air temperature is between (273.15-275.15) K (0-2) °C. Methods: The data for this study, which includes Temperature (T), Snow Albedo (SA), and Snow Density (SD) as monthly-daily mean, taken from the European Center for Medium-Range Weather Forecasts (ECMWF) for fifteen years from 2008 to 2022 for several selected stations over northern Iraq. The method was to take the monthly rates of snow density, snow albedo, and temperature for the stations of Erbil, Sulaymaniyah, Zakho, Dohuk, and Amadiyah, and the type of relationship and strength of the connection between them was also known. Results: The study found an inverse relationship between snow albedo and snow density across the selected stations, indicating that an increase in snow density leads to a decrease in snow albedo. Notably, Duhok City exhibited the strongest relationship between snow albedo and density, with a regression coefficient of 0.9699 compared to other regions. Conclusions: This study highlights the complex relationship between snow albedo and density in northern Iraq. The strong correlation observed in Duhok City suggests the importance of further research to understand the factors influencing snow properties in this region. [ABSTRACT FROM AUTHOR]