European Centre For Medium-Range Weather Forecasts Research Articles

Spatio-temporal analysis of rainfall in relation to monsoon teleconnections and agriculture at Regional Scale in Haryana, India.

This study examined the long-term (1980-2019) spatio-temporal trends, variability and teleconnections of Indian summer monsoon rainfall (ISMR) of all districts of Haryana, India and their impact on agricultural productivity. The gridded datasets of India Meteorological Department (IMD) were used to statistically analyse the rainfall distribution, trend, coefficient of variation and intensity of rainfall. The gridded datasets of European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis V5 (ERA5) were examined for lower and upper tropospheric wind circulation (850hPa and 200hpa), vertically integrated moisture transport (VIMT) and surface moisture flux (SMF). The datasets of National Oceanic and Atmospheric Administration (NOAA) were correlated with ISMR and composite deviation of rainfall and rainfall intensity during El Niño and La Niña from neutral years was examined at district level. Our analysis revealed that districts lying in eastern agroclimatic zone (EAZ) of Haryana received more ISMR during each month of monsoon season as compared to the ones situated in western agroclimatic zone (WAZ). Trend-free pre-whitening Mann-Kendall (TFPW-MK) test revealed that Kurukshetra, Panipat, Ambala, Rohtak, Faridabad, Jhajjar, Sonipat, Fatehabad and Palwal have shown a decreasing trend while Mahendragarh and Panchkula have shown an increasing trend of rainfall. During the El Niño years, most of the locations in the state received deficient to large deficient category, whereas during the La Niña episodes, most of the locations received excess to large excess category of ISMR, which is indicative of the influence of El Niño-Southern Oscillation (ENSO) on the regional scale. The influence of ISMR on bajra productivity for the districts lying in WAZ and rice productivity for the districts lying in EAZ was undertaken. This study is beneficial for understanding the impacts of climate change and climate variability on ISMR dynamics in Haryana which may further guide the policy-makers and beneficiaries for optimising the use of hydrological resources.

The Copernicus Atmosphere Monitoring Service: From Research to Operations

Abstract The Copernicus Atmosphere Monitoring Service (CAMS), part of the European Union’s Earth observation program Copernicus, entered operations in July 2015. Implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) as a truly European effort with over 23,500 direct data users and well over 200 million end users worldwide as of March 2022, CAMS delivers numerous global and regional information products about air quality, inventory-based emissions and observation-based surface fluxes of greenhouse gases and from biomass burning, solar energy, ozone and UV radiation, and climate forcings. Access to CAMS products is open and free of charge via the Atmosphere Data Store. The CAMS global atmospheric composition analyses, forecasts, and reanalyses build on ECMWF’s Integrated Forecasting System (IFS) and exploit over 90 different satellite data streams. The global products are complemented by coherent higher-resolution regional air quality products over Europe derived from multisystem analyses and forecasts. CAMS information products also include policy support such as quantitative impact assessment of short- and long-term pollutant-emission mitigation scenarios, source apportionment information, and annual European air quality assessment reports. Relevant CAMS products are cited and used for instance in IPCC Assessment Reports. Providing dedicated support for users operating smartphone applications, websites, or TV bulletins in Europe and worldwide is also integral to the service. This paper presents key achievements of the CAMS initial phase (2014–21) and outlines some of its new components for the second phase (2021–28), e.g., the new Copernicus anthropogenic CO2 emissions Monitoring and Verification Support capacity that will monitor global anthropogenic emissions of key greenhouse gases.

The Copernicus Climate Change Service: Climate Science in Action

Abstract The Copernicus Climate Change Service (C3S) provides open and free access to state-of-the-art climate data and tools for use by governments, public authorities, and private entities around the world. It is fully funded by the European Union and implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) together with public and private entities in Europe and elsewhere. With over 120,000 registered users worldwide, C3S has rapidly become an authoritative climate service in Europe and beyond, delivering quality-assured climate data and information based on the latest science. Established in 2014, C3S became fully operational in 2018 with the launch of its Climate Data Store, a powerful cloud-based infrastructure providing access to a vast range of global and regional information, including climate data records derived from observations, the latest ECMWF reanalyses, seasonal forecast data from multiple providers, and a large collection of climate projections. The system has been designed to be accessible to nonspecialists, offering a uniform interface to all data and documentation as well as a Python-based toolbox that can be used to process and use the data online. C3S publishes European State of the Climate reports annually for policy-makers, as well as monthly and annual summaries that are widely disseminated in the international press. Together with users, C3S develops customized indicators of climate impacts in economic sectors such as energy, water management, agriculture, insurance, health, and urban planning. C3S works closely with national climate service providers, satellite agencies, and other stakeholders on the improvement of its data and services.

Spatial connections in extreme precipitation events obtained from NWP forecasts: A complex network approach

Understanding the spatial variability of extreme precipitation events (EPEs) has always been a challenging task, with climate change further complicating the issue. Many studies have looked into the spatiotemporal characteristics of EPEs and the possibility of them being connected. Most of these studies are based on observation data products which inform about the events after they have occurred. Various Quantitative Precipitation Forecasts (QPFs) obtained from the Numerical Weather Prediction (NWP) models such as those of the European Centre for Medium-range Weather Forecasts (ECMWF), Japan Meteorological Agency (JMA), National Centre for Medium-Range Weather Forecasting (NCMRWF), and UK Met Office (UKMO) are available which inform about the precipitation events before they have occurred. In this study, we inter-evaluate four gridded (deterministic) QPFs over the Ganga River basin of India for their ability to detect the spatial connections among EPEs during the monsoon months, i.e., June, July, August and September (JJAS). Several experimental runs are performed in multiple time periods (13, 6 and 4 years) at various percentile thresholds (85th, 90th and 95th percentile) at two spatial resolutions of 25 and 50-km. Moreover, we also evaluate the forecast patterns of the QPF obtained from the national agency of India, i.e., NCMRWF and analyse its performance with respect to the other three selected QPFs from international agencies. For the observation/reference dataset, the gridded Indian Meteorological Department (IMD) dataset is selected. We first use the deterministic and dichotomous error statistics to evaluate the performance of NWP forecasts. Further, we employ the theory of complex networks to create networks of extreme precipitation at each grid. With this work, we aim to answer (a) can the theory of complex networks be a feasible tool for evaluating the performance of NWP forecasts in detecting extreme precipitation behaviour; (b) does the change in temporal length of the NWP forecasts influence the spatial structure of extreme precipitation as observed before, and (c) can a global NWP model, larger in length, be used as a potential substitute to the NCMRWF model. The ECMWF model performs well with respect to IMD as the average CC for TP_13 is 0.42 while they are 0.31 and 0.34 for TP_6 and TP_4, respectively. Also, we find that ECMWF can be a suitable substitute for the NCMRWF model. The identification of a substitute precipitation forecast, larger in temporal length, can be beneficial as input in hydrological models for streamflow forecasting and estimating parameters for bias correction of precipitation, among others.

Investigation of Waves Generated by Tropical Cyclone Kyarr in the Arabian Sea: An Application of ERA5 Reanalysis Wind Data

In this study, the wave conditions in the Arabian Sea induced by tropical cyclone Kyarr (2019) have been simulated by employing the 3rd generation wave model MIKE 21 SW. The model was run from 24 October to 1 November 2019, a total of 8 days. The MIKE 21 SW model was forced by reanalyzed ERA5 wind data from the European Centre for Medium-Range Weather Forecasts (ECMWF). The results are compared with buoy data from the Indian National Centre for Ocean Information Services (INCOIS), which is located at 67.44° E, 18.50° N. In addition, the satellite altimeter data (CryoSat-2, SARAL and Jason-3 satellite altimeter data) was utilized for validation. Three wave parameters are considered for the validation: the significant wave height; the peak wave period; and the mean wave direction. The validation results showed that the significant wave height, the peak wave period, and the mean wave direction could be reasonably predicted by the model with reanalysis wind data as input. The maximum significant wave height reached to 10.7 m (with an associated peak wave period of 12.5 s) on 28 October 2019 at 23:00:00 in the middle of the Arabian Sea. For coastal areas, the significant wave height along the Iran and Pakistan (north Arabian Sea) coasts increased to a range of 1.4–2.8 m when tropical cyclone Kyarr moved northward. This wave height along with elevated sea level may cause severe coastal erosion and nearshore inland flooding. Impacts of cyclones on coastal zones critical facilities and infrastructure can be reduced by timely and suitable action before the event, so coastal managers should understand the effect of cyclones and their destructive consequences. The validated model developed in this study may be utilized as input data of evaluating the risk to life and infrastructure in this area.

Assessment of Wave Energy Resources in China

The evolution of renewable energy technologies may surmount fossil fuel disadvantages. Wave energy is considered one of the best alternatives to fossil energy due to its advantages. The 40-year (1979–2018) spatio-temporal distribution of wave energy is presented using European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) datasets. In addition, the common formula used for wave energy evaluation is derived from the linear wave theory in deep water, which is not applicable in shallow water. Therefore, a new equation that is suitable for both shallow and deep water is derived, which improves the accuracy of the wave energy evaluation for various water depths. The main aim is to investigate the spatio-temporal wave energy for offshore China from 1979 to 2018 and combine the new standard of classification to recommend the optimal area. The nearshore zone from Zhejiang province to Guandong province is considered the ideal zone, and the average annual wave energy density in this area is above 10 kW/m.

Two types of cold waves affecting northeast China and the corresponding different key regions of precedent sea ice and sea surface temperature

AbstractBased on the daily minimum temperature data from 199 meteorological stations in northeast China (NEC), the cold wave (CW) processes in winter from 1980 to 2020 are identified. Then, by using the reanalysis 500‐hPa geopotential height data provided by the European Centre for Medium‐Range Weather Forecasts (ECMWF), the CWs are objectively classified into two types (abbreviated as Type 1 and Type 2) according to the corresponding atmospheric circulation through the hierarchical cluster algorithm (HCA). The relationships of the CWs with the Arctic sea sic (SIC) and sea surface temperature (SST) are revealed. For Type 1, the SIC in the north of the Barents Sea–Kara Sea is relatively less in the previous autumn, and a wave train propagating from the polar regions to Northeast Asia is stimulated under the ice–atmosphere interaction. This wave train further cooperates with the positive SST anomaly in the southern Indian Ocean to excite the low‐vortex circulation under the negative phase of Arctic Oscillation (AO) in winter, which is conducive to the occurrence of Type 1. However, the relatively low SST in the western Pacific warm pool in previous autumn stimulates a northward propagating wave train along the meridian, which increases the degree of meridionality of the atmospheric circulation over East Asia and thus indirectly causes the polar‐vortex‐splitting circulation under the positive AO phase, which favours the formation of Type 2. The numerical simulations can well reproduce the observational facts, further confirming the possible impact of the SIC and SST in the previous autumn on the two types of CW over the NEC.

Feasibility Study of Wave Power in Ghana

ABSTRACT In this research, the evaluation of Ghana’s wave energy potential was presented based on the ERA5 monthly averaged data on single levels from 1979 to 2020 from European Centre for Medium-Range Weather Forecast (ECMWF). The data obtained which was the Significant Wave Height and Wave Period were used to calculate the Average Wave Power and Energy. The total wave power crossing the three divisions of Ghana’s coastline has been calculated to be 7215 MW. The calculated wave energy available was compared to the Annual Energy Demand of Ghana. The calculated value gives a clear picture that wave energy can be another renewable energy source in Ghana. This was done relying on data from a third-generation spectral wave model to simulate the wave conditions which were used to analyse the potential of the wave-generated energy of the selected coastal areas in Ghana.

Evaluation of Tianji and ECMWF high-resolution precipitation forecasts for extreme rainfall event in Henan in July 2021

The extreme rainfall event of July 17 to 22, 2021 in Henan Province, China, led to severe urban waterlogging and flood disasters. This study investigated the performance of high-resolution weather forecasts in predicting this extreme event and the feasibility of weather forecast-based hydrological forecasts. To achieve this goal, high-resolution precipitation forecasts from the Tianji weather system and the forecast system of the European Centre for Medium-Range Weather Forecasts (ECMWF) were evaluated with the spatial verification metrics of structure, amplitude, and location. The results showed that Tianji weather forecasts accurately predicted the amplitude of 12-h accumulated precipitation with a lead time of 12 h. The location and structure of the rainfall areas in Tianji forecasts were closer to the observations than ECMWF forecasts. Tianji hourly precipitation forecasts were also more accurate than ECMWF hourly forecasts, especially at lead times shorter than 8 h. The precipitation forecasts were used as the inputs to a hydrological model to evaluate their hydrological applications. The results showed that the runoff forecasts driven by Tianji weather forecasts could effectively predict the extreme flood event. The runoff forecasts driven by Tianji forecasts were more accurate than those driven by ECMWF forecasts in terms of amplitude and location. This study demonstrates that high-resolution weather forecasts and corresponding hydrological forecasts can provide valuable information in advance for disaster warnings and leave time for people to act on the event. The results encourage further hydrological applications of high-resolution weather forecasts, such as Tianji weather forecasts, in the future.

Evaluation of ERA5, ERA‐Interim, JRA55 and MERRA2 reanalysis precipitation datasets over the Poyang Lake Basin in China

AbstractIncreasing variability of precipitation induced by climate change raises uncertainties for ecosystem services and agricultural production in the Poyang Lake Basin (PLB), China. Reanalysis precipitation datasets (RPDs) are attractive alternatives for monitoring the hydro‐climate cycles. However, their applicability in China has mainly been evaluated at the national scale, while less attention has been paid to the PLB or southeastern China. Here, we apply various metrics to evaluate the magnitude and spatial similarity of annual, monthly, different intensities and the spatial structure of precipitation from four reanalysis—the fifth global atmospheric analysis dataset of the European Center for Medium‐Range Weather Forecasts (ECMWF) (ERA5), Interim ECMWF Reanalysis (ERA‐Interim), Japanese 55‐year Reanalysis project (JRA55) and Modern‐Era Retrospective analysis for Research and Applications, Version 2 (MERRA‐2), against the rain‐gauges over the PLB during 1980–2018. The results showed that the detection capacity of ERA5 and MERRA2 shifted around 2002, whereas that of ERA‐Interim and JRA55 did not substantially change. Before 2002, ERA‐Interim had the lowest biases for the magnitude of annual, monthly and different intensities of precipitation (except for heavy rain), while ERA5 and MERRA2 had strengths in capturing spatial correlation. Notably, ERA5 and MERRA2 had region‐wide wet and dry biases, respectively, before 2002, which can be attributed mainly to the estimation of flood season precipitation (from April to June) and heavy rain. In contrast, the fractional contribution of light–moderate rain was underestimated and overestimated by ERA5 and MERRA2, respectively. However, after 2002, because of the improved reproducibility of flood season precipitation and heavy rain magnitude, the systematic biases of ERA5 and MERRA2 significantly decreased, especially for ERA5, which outperformed other RPDs on all aspects of precipitation. The conclusions of this paper suggest that the variation of applicability over time should be seriously tested before the utilization of RPDs in hydro‐climate analyses.

Evaluation of the N2O Rate of Change to Understand the Stratospheric Brewer-Dobson Circulation in a Chemistry-Climate Model.

The Brewer-Dobson Circulation (BDC) determines the distribution of long-lived tracers in the stratosphere; therefore, their changes can be used to diagnose changes in the BDC. We evaluate decadal (2005-2018) trends of nitrous oxide (N2O) in two versions of the Whole Atmosphere Chemistry-Climate Model (WACCM) by comparing them with measurements from four Fourier transform infrared (FTIR) ground-based instruments, the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS), and with a chemistry-transport model (CTM) driven by four different reanalyses. The limited sensitivity of the FTIR instruments can hide negative N2O trends in the mid-stratosphere because of the large increase in the lowermost stratosphere. When applying ACE-FTS measurement sampling on model datasets, the reanalyses from the European Center for Medium Range Weather Forecast (ECMWF) compare best with ACE-FTS, but the N2O trends are consistently exaggerated. The N2O trends obtained with WACCM disagree with those obtained from ACE-FTS, but the new WACCM version performs better than the previous above the Southern Hemisphere in the stratosphere. Model sensitivity tests show that the decadal N2O trends reflect changes in the stratospheric transport. We further investigate the N2O Transformed Eulerian Mean (TEM) budget in WACCM and in the CTM simulation driven by the latest ECMWF reanalysis. The TEM analysis shows that enhanced advection affects the stratospheric N2O trends in the Tropics. While no ideal observational dataset currently exists, this model study of N2O trends still provides new insights about the BDC and its changes because of the contribution from relevant sensitivity tests and the TEM analysis.

Quality control and error assessment of the Aeolus L2B wind results from the Joint Aeolus Tropical Atlantic Campaign

Abstract. Since the start of the European Space Agency's Aeolus mission in 2018, various studies were dedicated to the evaluation of its wind data quality and particularly to the determination of the systematic and random errors in the Rayleigh-clear and Mie-cloudy wind results provided in the Aeolus Level-2B (L2B) product. The quality control (QC) schemes applied in the analyses mostly rely on the estimated error (EE), reported in the L2B data, using different and often subjectively chosen thresholds for rejecting data outliers, thus hampering the comparability of different validation studies. This work gives insight into the calculation of the EE for the two receiver channels and reveals its limitations as a measure of the actual wind error due to its spatial and temporal variability. It is demonstrated that a precise error assessment of the Aeolus winds necessitates a careful statistical analysis, including a rigorous screening for gross errors to be compliant with the error definitions formulated in the Aeolus mission requirements. To this end, the modified Z score and normal quantile plots are shown to be useful statistical tools for effectively eliminating gross errors and for evaluating the normality of the wind error distribution in dependence on the applied QC scheme, respectively. The influence of different QC approaches and thresholds on key statistical parameters is discussed in the context of the Joint Aeolus Tropical Atlantic Campaign (JATAC), which was conducted in Cabo Verde in September 2021. Aeolus winds are compared against model background data from the European Centre for Medium-Range Weather Forecasts (ECMWF) before the assimilation of Aeolus winds and against wind data measured with the 2 µm heterodyne detection Doppler wind lidar (DWL) aboard the Falcon aircraft. The two studies make evident that the error distribution of the Mie-cloudy winds is strongly skewed with a preponderance of positively biased wind results distorting the statistics if not filtered out properly. Effective outlier removal is accomplished by applying a two-step QC based on the EE and the modified Z score, thereby ensuring an error distribution with a high degree of normality while retaining a large portion of wind results from the original dataset. After the utilization of the described QC approach, the systematic errors in the L2B Rayleigh-clear and Mie-cloudy winds are determined to be below 0.3 m s−1 with respect to both the ECMWF model background and the 2 µm DWL. Differences in the random errors relative to the two reference datasets (Mie vs. model is 5.3 m s−1, Mie vs. DWL is 4.1 m s−1, Rayleigh vs. model is 7.8 m s−1, and Rayleigh vs. DWL is 8.2 m s−1) are elaborated in the text.

Dynamic-LSTM hybrid models to improve seasonal drought predictions over China

Accurate drought prediction is essential for drought resilience and water resources management. The skill of seasonal drought prediction from dynamical and statistical models has limitations. This study combines dynamical models and deep learning to construct hybrid (dynamical-statistical) models. We use the random forest to extract typical grids based on the geopotential height, sea-level pressure, and 2-m temperature. The long short-term memory (LSTM) is used to construct the statistical models, with atmospheric variables as predictors and the 3-month Standardized Precipitation Index (SPI3) as the predictand. The hindcasts of atmospheric variables from the European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5 model are processed as predictors to force the statistical models. The hybrid model, constructed using the dynamical models and the LSTM, is named dynamic-LSTM (“D-LSTM”). The results suggest that the LSTM models are of information-extraction capability and robustness. When the lead time exceeds one month, the prediction skills are significantly improved by the D-LSTM models, especially in the East, Northwest, Southwest, and Tibet. In most regions, the D-LSTM models are more skillful across all seasons for lead times exceeding 30 days and are reliable in predicting droughts in spring and summer when the ECMWF SEAS5 loses skills at the seasonal scale. Furthermore, the D-LSTM models are more accurate in drought onset prediction.

Evaluation of Future Simulations of the CMIP5 GCMs Concerning Boreal Wintertime Atmospheric Teleconnection Patterns

In this study, a pattern detection method is applied on the RCP4.5 and RCP8.5 simulation outputs of seven GCMs—disseminated by the Coupled Model Intercomparison Project Phase 5 (CMIP5)—to determine whether atmospheric teleconnection patterns detected in the ERA-20C reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) will be observable in the future projections of the CMIP5 GCMs. The pattern detection technique—which combines the negative extrema method and receiver operating characteristic (ROC) curve analysis—is used on the geopotential height field at the 500 hPa pressure level in wintertime, in the Northern Hemisphere. It was found that teleconnections obtained from the ERA-20C reanalysis dataset for the period of 1976–2005 remain observable in the majority of the GCM outputs under the RCP4.5 and RCP8.5 scenarios for the periods of 2006–2035, 2021–2050, and 2071–2100. The results imply that atmospheric internal variability is the major factor that controls the teleconnections rather than the impact of radiative forcing.

A study on loss function against data imbalance in deep learning correction of precipitation forecasts

Sample imbalance has always been an important issue to be solved in heavy precipitation forecasts. Introducing appropriate constraints into the loss function of a machine learning model can mitigate the negative impact of sample imbalance on model training. This study explores the role of Dice loss function in dealing with sample imbalance and verifies its application on the correction of heavy precipitation forecasts using the U-Net neural network. On this basis, the application of ordinal classification in forecasts correction is further verified. The results show that the concept of Dice loss is highly similar to that of threat score, which can suppress the negative impact of sample imbalance on heavy precipitation forecasts by ignoring most of the events that are insensitive to heavy precipitation forecasts. Model correction experiments further indicate that the correction models trained with weighted cross entropy, differentiable threat score loss and Dice loss (WCE, DTS and Dice models) improve the precipitation forecasts of ECMWF (European Centre for Medium-Range Weather Forecasts) model. For the forecasts of all precipitation levels, Dice model effectively suppresses false alarms while improving hits, whereas WCE model greatly promotes false alarms while significantly improving hits. In contrast, DTS model significantly suppresses large precipitation false alarms while improving its hits, but causes substantial small precipitation false alarms while significantly promoting its hits. As a result, for large precipitation forecasts, both Dice and DTS models perform significantly better than WCE model, while for small precipitation forecasts, Dice model performs better than DTS and WCE models. Furthermore, the correction model combining ordinal classification and Dice loss exhibits the best forecasting skill. This model further suppresses the false alarms of large precipitation while improving its hits.

SSAS: Spatiotemporal Scale Adaptive Selection for Improving Bias Correction on Precipitation.

By utilizing physical models of the atmosphere collected from the current weather conditions, the numerical weather prediction model developed by the European Centre for Medium-range Weather Forecasts (ECMWF) can provide the indicators of severe weather such as heavy precipitation for an early-warning system. However, the performance of precipitation forecasts from ECMWF often suffers from considerable prediction biases due to the high complexity and uncertainty for the formation of precipitation. The bias correcting on precipitation (BCoP) was thus utilized for correcting these biases via forecasting variables, including the historical observations and variables of precipitation, and these variables, as predictors, from ECMWF are highly relevant to precipitation. The existing BCoP methods, such as model output statistics and ordinal boosting autoencoder, do not take advantage of both spatiotemporal (ST) dependencies of precipitation and scales of related predictors that can change with different precipitation. We propose an end-to-end deep-learning BCoP model, called the ST scale adaptive selection (SSAS) model, to automatically select the ST scales of the predictors via ST Scale-Selection Modules (S3M/TS2M) for acquiring the optimal high-level ST representations. Qualitative and quantitative experiments carried out on two benchmark datasets indicate that SSAS can achieve state-of-the-art performance, compared with 11 published BCoP methods, especially on heavy precipitation.

The Diurnal Variation of the Evaporation Duct Height and Its Relationship With Environmental Variables in the South China Sea

Based on data from the European Centre for Medium-Range Weather Forecasts (ECMWF) ECWMF ERA5 Reanalysis (ERA-5) and the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) datasets, the diurnal variation of the evaporation duct height (EDH) in the South China Sea (SCS) and its relationship with environmental variables using the Naval Postgraduate School (NPS) model are analyzed, discussed, and presented. The findings are given as follows. First, the EDH diurnal variations are primarily influenced by the sea-land thermal contrast, with the major influencing variables being the relative humidity (RH) and the near-surface wind speed (WS), followed by the air-sea temperature difference (ASTD). Secondly, the areas where the EDH and the RH are highly correlated are mostly in northern SCS, and the two variables are negatively correlated. Besides, in southeastern SCS, the EDH has a greater and positive correlation with the WS. Seasonal changes can be found in areas where the EDH is highly correlated with the ASTD, primarily to the northwest of Luzon Island in winter and the east coast of Vietnam in summer. Finally, the spatial patterns of the EDH first mode in the SCS present a monopolar structure with the strong loading in northern SCS.

Evaluation of the Forecast Performance for Week-2 Winter Surface Air Temperature from the Model for Prediction Across Scales–Atmosphere (MPAS-A)

Abstract The forecast skill for week-2 wintertime surface air temperature (SAT) over the Northern Hemisphere by the Model for Prediction Across Scales–Atmosphere (MPAS-A) is evaluated and compared with operational forecast systems that participate in the Subseasonal to Seasonal Prediction project (S2S). An intercomparison of the MPAS against the China Meteorological Administration (CMA) model and the European Centre for Medium-Range Weather Forecasts (ECMWF) model was performed using 10-yr reforecasts. Comparing the forecast skill for SAT and atmospheric circulation anomalies at a lead of 2 weeks among the three models, the MPAS shows skill lower than the ECMWF model but higher than the CMA model. The gap in skills between the MPAS model and CMA model is not as large as that between the ECMWF model and MPAS model. Additionally, an intercomparison of the MPAS model against 10 S2S models is presented by using real-time forecasts since 2016 stored in the S2S database. The results show that the MPAS model has forecast skill for week-2 to week-4 wintertime SAT comparable to that in most S2S models. The MPAS model tends to be at an intermediate level compared to current operational forecast models.

Climate change impacts the future offshore wind energy resources in India: Evidence drawn from CORDEX-SA Regional Climate Models

Harnessing offshore wind energy is a potential solution to meet rising energy demand. Concurrently, exploitable wind energy is susceptible to climate change. In this study, an ensemble of six RCMs derived from Coordinated Regional Climate Downscaling Experiment — South Asia (CORDEX-SA) regional climate models is created to study future variation of wind speed and wind power density. The variation with respective to the historic time periods is examined for near-future (2020–2046), mid-future (2047–73), and far-future time periods (2074–2099). Two Representative Concentration Pathways (RCP) scenarios, such as RCP4.5 and RCP8.5, are considered for the analysis. The credibility of RCM data is assessed by comparing it with European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) reanalysis dataset which is validated against buoy datasets. Findings reveal that annual mean wind speed in the considered period ranges between 2% and −8% in the case of RCP4.5 while it is 7% and −16% in the RCP8.5 scenario. Seasonal percentage variation in WPD is between 80% and −56% in the RCP4.5 scenario, and it is relatively higher (105% and −75%) in the RCP8.5 scenario. By 2099, it is expected that WPD may increase by 10% and 30% in the southern coast, whereas 20% and 40% decrement is expected near the coast of Gujarat for RCP4.5 and RCP8.5 scenarios, respectively. The findings of this study enable stakeholders to harness wind energy in the Indian offshore region.

Global Climatology of Extratropical Cyclones From a New Tracking Approach and Associated Wave Heights From Satellite Radar Altimeter

AbstractExtratropical cyclones (ECs) produce comparable wave heights and hazardous sea states to those under hurricanes. With strong wind speeds and considerably larger diameters, the impact of ECs on the global wave climate is substantial. Because ECs occur more frequently, move more quickly, and exist in a large variety of sizes and shapes, these storms are not as well documented. Here, we present findings from a global EC tracking algorithm aimed at creating an EC database of wave producing storms. We focus on the major ocean basins of extratropical cyclone activity: The North Atlantic, North Pacific, and Southern Oceans. Our tracking algorithm uses mean sea level pressure fields from the European Centre for Medium‐Range Weather Forecasts (ECMWF) high resolution, advanced data assimilating, ERA5 reanalysis over the years 1979–2020. The 42 year‐long data set of thousands of cyclone tracks allows seasonal patterns and longer‐term trends of EC activity to be analyzed in these ocean regions. In addition, we employ 36 years of satellite radar altimeter‐derived significant wave height (Hs) data from 14 inter‐calibrated satellite missions to study the wave climate induced by the identified ECs in each basin. The resulting wave climatology compares well with previous results in the mid to high latitudes. We then analyze average wave heights under different EC characteristics, including cyclone translational speed and maximum winds, which have strong effects on wave height variability. Finally, a brief analysis of EC and wave climate under positive and negative phases of the North Atlantic Oscillation is presented.