European Centre For Medium-Range Weather Forecasts Research Articles

Hybrid CMOD-Diffusion Algorithm Applied to Sentinel-1 for More Robust and Precise Wind Retrieval

Synthetic Aperture Radar (SAR) imagery presents significant advantages for observing ocean surface winds owing to its high spatial resolution and low sensitivity to extreme weather conditions. Nevertheless, signal noise poses a challenge, hindering precise wind retrieval from SAR imagery. Moreover, traditional geophysical model functions (GMFs) often falter, particularly in accurately estimating high wind speeds, notably during extreme weather phenomena like tropical cyclones (TCs). To address these limitations, this study proposes a novel hybrid model, CMOD-Diffusion, which integrates the strengths of GMFs with data-driven deep learning methods, thereby achieving enhanced accuracy and robustness in wind retrieval. Based on the coarse estimation of wind speed by the traditional GMF CMOD5.N, we introduce the recently developed data-driven method Denoising Diffusion Probabilistic Model (DDPM). It transforms an image from one domain to another domain by gradually adding Gaussian noise, thus achieving denoising and image synthesis. By introducing the DDPM, the noise from the observed normalized radar cross-section (NRCS) and the residual of the GMF methods can be largely compensated. Specifically, for wind speeds within the low-to-medium range, a DDPM is employed before proceeding to another CMOD iteration to recalibrate the observed NRCS. Conversely, a posterior-placed DDPM is applied after CMOD to reconstruct high-wind-speed regions or TC-affected areas, with the prior information from regions characterized by low wind speeds and recalibrated NRCS values. The efficacy of the proposed model is evaluated by using Sentinel-1 SAR imagery in vertical–vertical (VV) polarization, collocated with data from the European Centre for Medium-Range Weather Forecasts (ECMWF). Experimental results based on validation sets demonstrate significant improvements over CMOD5.N, particularly in low-to-medium wind speed regions, with the Structural Similarity Index (SSIM) increasing from 0.76 to 0.98 and the Root Mean Square Error (RMSE) decreasing from 1.98 to 0.63. Across the entire wind field, including regions with high wind speeds, the validation data obtained through the proposed method exhibit an RMSE of 2.39 m/s, with a correlation coefficient of 0.979.

Effectiveness of inter-regional collaborative emission reduction for ozone mitigation under local-dominated and transport-affected synoptic patterns.

In recent years, the concentrations of ozone and the pollution days with ozone as the primary pollutant have been increasing year by year. The sources of regional ozone mainly depend on local photochemical formation and transboundary transport. The latter is influenced by different weather circulations. How to effectively reduce the inter-regional emission to control ozone pollution under different atmospheric circulation is rarely reported. In this study, we classify the atmospheric circulation of ozone pollution days from 2014 to 2019 over Central China based on the Lamb-Jenkinson method and the global analysis data of the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5) operation. The effectiveness of emission control to alleviate ozone pollution under different atmospheric circulation is simulated by the WRF-Chem model. Among the 26 types of circulation patterns, 9 types of pollution days account for 79.5% of the total pollution days and further classified into 5 types. The local types (A and C type) are characterized by low surface wind speed and stable weather conditions over Central China due to a high-pressure system or a southwest vortex low-pressure system, blocking the diffusion of pollutants. Sensitivity simulations of A-type show that this heavy pollution process is mainly contributed by local emission sources. Removing the anthropogenic emission of pollutants over Central China would reduce the ozone concentration by 39.1%. The other three circulation patterns show pollution of transport characteristics affected by easterly, northerly, or southerly winds (N-EC, EC, S-EC-type). Under the EC-type, removing anthropogenic pollutants of East China would reduce the ozone concentration by 22.7% in Central China.

A machine learning model that outperforms conventional global subseasonal forecast models

Skillful subseasonal forecasts are crucial for various sectors of society but pose a grand scientific challenge. Recently, machine learning-based weather forecasting models outperform the most successful numerical weather predictions generated by the European Centre for Medium-Range Weather Forecasts (ECMWF), but have not yet surpassed conventional models at subseasonal timescales. This paper introduces FuXi Subseasonal-to-Seasonal (FuXi-S2S), a machine learning model that provides global daily mean forecasts up to 42 days, encompassing five upper-air atmospheric variables at 13 pressure levels and 11 surface variables. FuXi-S2S, trained on 72 years of daily statistics from ECMWF ERA5 reanalysis data, outperforms the ECMWF’s state-of-the-art Subseasonal-to-Seasonal model in ensemble mean and ensemble forecasts for total precipitation and outgoing longwave radiation, notably enhancing global precipitation forecast. The improved performance of FuXi-S2S can be primarily attributed to its superior capability to capture forecast uncertainty and accurately predict the Madden-Julian Oscillation (MJO), extending the skillful MJO prediction from 30 days to 36 days. Moreover, FuXi-S2S not only captures realistic teleconnections associated with the MJO but also emerges as a valuable tool for discovering precursor signals, offering researchers insights and potentially establishing a new paradigm in Earth system science research.

Indication for Biases in Dry Intrusions and the Marine Boundary Layer Over the Azores in ECMWF Short‐Term Forecasts and Analyses

AbstractThe model representation of dry intrusions (DIs) and the marine boundary layer (MBL) is analyzed in the European Centre for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). For this purpose, a DI classification at the Azores is combined with observation, short‐term background forecast and analysis data from the IFS data assimilation system. The background exhibits a cold bias in the descending DI, which is possibly related to a cold bias in the MBL below through vertical mixing. At the surface, simulated wind speeds are underestimated and directions are veered compared to the observations. The errors are reduced in the analysis except for near‐surface wind and humidity biases. We hypothesize that these biases are connected through underestimated surface latent heat fluxes. Such persistent biases potentially influence local weather and midlatitude weather evolution as cyclones are supplied with moisture from the cold sector influenced by DIs.

Subseasonal variability of sea level pressure and its influence on snowpack over mid-high-latitude Eurasia during boreal winter

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.

Insights from very‐large‐ensemble data assimilation experiments with a high‐resolution general circulation model of the Red Sea

AbstractEnsemble 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.

The impact of El Niño–Southern Oscillation on the total column ozone over the Tibetan Plateau

Abstract. The Tibetan Plateau (TP; approximately 27.5–37.5° N, 75.5–105.5° E) is the highest and largest plateau on Earth with a mean elevation of over 4 km. This special geography causes strong surface solar ultraviolet radiation (UV), with potential risks to human and ecosystem health, which is mainly controlled by the local stratospheric ozone concentration. The El Niño–Southern Oscillation (ENSO), the dominant mode of interannual variability on Earth, is characterised by the tropical Pacific Ocean sea surface temperature anomalies (SSTAs) and sea level pressure change for the warm-phase El Niño and cold-phase La Niña events. Although some studies have suggested the existence of positive correlation between ENSO and the total column ozone (TCO) over the TP, the mechanism underlying this effect is not fully understood. Here we use the Copernicus Climate Change Service (C3S) merged satellite dataset, the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) dataset and the TOMCAT three-dimensional (3D) offline chemical transport model forced by ERA5 meteorological reanalyses from the European Centre for Medium-Range Weather Forecasts (ECMWF) over the period 1979–2021 to investigate the influence of ENSO on the TCO over the TP. We find that the El Niño (La Niña) events favour positive (negative) TCO anomalies over the TP from wintertime of its mature phase to springtime of its decaying phase. Through studying the ozone profile, we attribute the positive (negative) TCO anomalies mainly to the increased (decreased) ozone at the 200–70 hPa levels, i.e. in the upper troposphere and lower stratosphere (UTLS) regions. Our results suggest that the El Niño events impact the TP TCO via the following potential processes: (1) a negative upper-level geopotential height anomaly associated with El Niño is responsible for a decrease in air column thickness; (2) the thickness decrease modulates reduced tropospheric temperature and thus favours a decrease in the tropopause height (TH); and (3) such a TH decrease tends to induce a change in the relative amounts of ozone-poor tropospheric and ozone-rich stratospheric air in the profile, which increases the partial column ozone in the UTLS and hence corresponds to the TP TCO increase. The La Niña events affect TP TCO in a manner resembling the El Niño events, except with anomalies of opposite sign. This work provides a systematic understanding of the influence of ENSO on ozone over the TP, which has implications for the interannual variability of ozone.

Multi-model ensembles for regional and national wheat yield forecasts in Argentina

Abstract While multi-model ensembles (MMEs) of seasonal climate models (SCMs) have been used for crop yield forecasting, there has not been a systematic attempt to select the most skillful SCMs to optimize the performance of a MME and improve in-season yield forecasts. Here, we propose a statistical model to forecast regional and national wheat yield variability from 1993–2016 over the main wheat production area in Argentina. Monthly mean temperature and precipitation from the four months (August–November) before harvest were used as features. The model was validated for end-of-season estimation in December using reanalysis data (ERA) from the European Centre for Medium-Range Weather Forecasts (ECMWF) as well as for in-season forecasts from June to November using a MME of three SCMs from 10 SCMs analyzed. A benchmark model for end-of-season yield estimation using ERA data achieved a R 2 of 0.33, a root-mean-square error (RMSE) of 9.8% and a receiver operating characteristic (ROC) score of 0.8 on national level. On regional level, the model demonstrated the best estimation accuracy in the northern sub-humid Pampas with a R 2 of 0.5, a RMSE of 12.6% and a ROC score of 0.9. Across all months of initialization, SCMs from the National Centers for Environmental Prediction, the National Center for Atmospheric Research and the Geophysical Fluid Dynamics Laboratory had the highest mean absolute error of forecasted features compared to ERA data. The most skillful in-season wheat yield forecasts were possible with a 3-member-MME, combining data from the SCMs of the ECMWF, the National Aeronautics and Space Administration and the French national meteorological service. This MME forecasted wheat yield on national level at the beginning of November, one month before harvest, with a R 2 of 0.32, a RMSE of 9.9% and a ROC score of 0.7. This approach can be applied to other crops and regions.

Raindrop size distribution characteristics of pre-monsoon precipitation observed over eastern India

The knowledge of raindrop size distribution (DSD) is crucial for understanding the microphysical processes involved with the precipitation. Different empirical relationships established with DSD parameters, like radar reflectivity– rainfall rate (Z–R) relationships and shape–slope (μ–Ʌ) relationships, can progress the rainfall estimation algorithms and cloud modeling simulations. In the present study, long-term (2018–2021) measurements of a Laser Precipitation Monitor (LPM) disdrometer installed at the National Institute of Technology, Rourkela, India is used to investigate the DSD characteristics of pre-monsoon (March–May) rainfall. Along with the disdrometer data, auxiliary parameters like convective available potential energy (CAPE), total column water vapor (TCWV), vertical profiles of temperature and relative humidity from reanalysis data sets of ECMWF (European Centre for Medium-Range Weather Forecasts) fifth-generation reanalysis (ERA5) are also used in this study. Based on standardized rainfall anomaly, the pre-monsoon precipitation days are classified into strong, moderate, and weak rainy days, and they contributed to 58.69%, 32.7%, and 8.61% of total rainfall, respectively. The average DSD indicated noteworthy variations among strong, moderate, and weak rainy days with maximum (minimum) concentration of raindrops in strong (weak) rainy days. The mean value of rain rate (R), normalized intercept parameter (Nw), and mass-weighted mean diameter (Dm) is maximum during days of strong rainfall. Strong rainy days showed high-value CAPE, TCWV and vertical profile of relative humidity. The majority of R is contributed by moderate-sized raindrops with a significant difference in the Z–R and μ–Λ relationships among three types of rainy days.

Evaluating the representation of Arctic cirrus solar radiative effects in the Integrated Forecasting System with airborne measurements

Abstract. In two case studies, airborne measurements of broadband solar irradiances above and below Arctic cirrus are compared to simulations of the Integrated Forecasting System (IFS) operated by the European Centre for Medium-Range Weather Forecasts (ECMWF) using offline runs of ECMWF's operational radiation scheme, “ecRad”. Furthermore, independent of the solar irradiances, cirrus properties are derived from collocated airborne active remote sensing observations to evaluate the optical and microphysical parameterizations in ecRad. The data were collected in the central Arctic over sea ice (81–90° N) with instrumentation installed aboard the High Altitude LOng range research aircraft (HALO) during a campaign in March and April 2022. Among others, the HALO instrumentation included upward- and downward-looking pyranometers to measure broadband solar irradiances, a cloud radar, and a multi-wavelength water vapour differential absorption lidar. Extended horizontal flight legs above and below single-layer cirrus were performed. The solar radiation measurements are used to evaluate ecRad in two case studies of optically thin and thick cirrus, with an average transmissivity of 0.9 and 0.6, respectively. Different ice optics parameterizations optionally available in ecRad are applied to test the match between simulation and measurements. Furthermore, the IFS-predicted ice water content and ice effective radius are replaced by values retrieved with the radar and lidar. The choice of ice optics parameterizations does not significantly improve the model–measurement agreement. However, introducing the retrieved ice microphysical properties brings measured and modelled irradiances in closer agreement for the optically thin cirrus, while the optically thick cirrus case is simulated as too thick. It is concluded that the ice water content simulated by the IFS is realistic and that the mismatch between observed and simulated solar irradiances mostly originates from the assumed or parameterized ice effective radius.

Global-scale evaluation of precipitation datasets for hydrological modelling

Abstract. Precipitation is the most important driver of the hydrological cycle, but it is challenging to estimate it over large scales from satellites and models. Here, we assessed the performance of six global and quasi-global high-resolution precipitation datasets (European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5), Climate Hazards group Infrared Precipitation with Stations version 2.0 (CHIRPS), Multi-Source Weighted-Ensemble Precipitation version 2.80 (MSWEP), TerraClimate (TERRA), Climate Prediction Centre Unified version 1.0 (CPCU), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR, hereafter PERCCDR) for hydrological modelling globally and quasi-globally. We forced the WBMsed global hydrological model with the precipitation datasets to simulate river discharge from 1983 to 2019 and evaluated the predicted discharge against 1825 hydrological stations worldwide, using a range of statistical methods. The results show large differences in the accuracy of discharge predictions when using different precipitation input datasets. Based on evaluation at annual, monthly, and daily timescales, MSWEP followed by ERA5 demonstrated a higher correlation (CC) and Kling–Gupta efficiency (KGE) than other datasets for more than 50 % of the stations, whilst ERA5 was the second-highest-performing dataset, and it showed the highest error and bias for about 20 % of the stations. PERCCDR is the least-well-performing dataset, with a bias of up to 99 % and a normalised root mean square error of up to 247 %. PERCCDR only show a higher KGE and CC than the other products for less than 10 % of the stations. Even though MSWEP provided the highest performance overall, our analysis reveals high spatial variability, meaning that it is important to consider other datasets in areas where MSWEP showed a lower performance. The results of this study provide guidance on the selection of precipitation datasets for modelling river discharge for a basin, region, or climatic zone as there is no single best precipitation dataset globally. Finally, the large discrepancy in the performance of the datasets in different parts of the world highlights the need to improve global precipitation data products.

Predicting malaria hyper endemic zones in West Africa using a regional scale dynamical malaria model

Due to the continuing large number of malaria-related deaths in tropical Africa, the need to develop a robust Malaria Early Warning System (MEWS) for effective action is growing to guide cost-effective implementation of interventions. This study employs a two-stage hierarchical evaluation technique to evaluate the ability of the VECTRI malaria model to simulate malaria dynamics at seasonal time scale (1 - 7 months) over Nigeria and West Africa. Two sets of malaria simulations are considered. The first set is based on VECTRI simulations driven by observed rainfall and temperature datasets (hereafter referred to as control run). The second is based on malaria simulations driven by the European Centre for Medium-Range Weather Forecasting (ECMWF) System5 ensemble seasonal forecasting system (hereafter referred to as Forecast run). Different metrics are employed to assess the skill of the VECTRI malaria model. Results based on the control run indicate that the model can reproduce hyper-endemic zones and the evolution of malaria cases, particularly the observed increase in cases with decreasing population density. Despite having significant biases and low correlation, the model successfully predicts annual anomalies in malaria cases across Nigeria, particularly in the savannah region that experience large malaria burden. Annual correlations between the VECTRI Forecast run and the VECTRI Control run are relatively low at all lead times (LT) and for each start date (SD) across West Africa, although correlation generally increases from the Gulf of Guinea to the Sahel. Despite low correlations, the Rank Probability Skill Score (RPSS) reveals that the model has a statistically significant skill in predicting malaria occurrences across all categories of malaria cases, regardless of start date or lead time. While the Guinea Forest has the strongest RPSS, the increase or decrease in skill from the first to seventh lead time varies significantly across the region. In addition, the VECTRI malaria model has a good ability to discriminate variability in malaria cases across all regions, with an average Area Under the Relative Operating Characteristics (ROC) Curve (AUC) of approximately 0.62. Our findings suggest that the VECTRI malaria model could be used as a reliable Malaria Early Warning System (MEWS), particularly for identifying malaria hyper-endemic zones in West Africa at seasonal time scale.

Towards unified land data assimilation at ECMWF: Soil and snow temperature analysis in the SEKF

AbstractWeather centres use a variety of data assimilation schemes to analyze different land variables in their operational forecast systems. Current activities at the European Centre for Medium‐Range Weather Forecasts (ECMWF) are working towards a unified and more consistent land data assimilation system to provide more accurate initial conditions for the atmospheric forecasts. The first step is to replace the current 1D optimal interpolation (1D‐OI) used for first‐layer soil and snow temperature analyses, and integrate multi‐layer soil and first‐layer snow temperature into the ensemble‐based simplified extended Kalman filter (SEKF) currently used for multi‐layer soil moisture. This work focuses on the technical developments and the evaluation of the atmospheric forecast skill of a series of numerical weather prediction experiments to compare different SEKF configurations with the former 1D‐OI over a three‐month summer and winter period. Using the SEKF leads to seasonally varying significant improvements in the 2‐m temperature forecast in the verification against own analyses and to slightly improved results in the validation using independent synoptic observations. This work lays the foundation for integrating additional land variables into the SEKF and investigating stronger land–atmosphere coupling.

Contrasting extremely warm and long-lasting cold air anomalies in the North Atlantic sector of the Arctic during the HALO-(𝒜 𝒞)3 campaign

Abstract. How air masses transform during meridional transport into and out of the Arctic is not well represented by numerical models. The airborne field campaign HALO-(𝒜𝒞)3 applied the High Altitude and Long-range Research Aircraft (HALO) within the framework of the collaborative research project on Arctic amplification (𝒜𝒞)3 to address this question by providing a comprehensive observational basis. The campaign took place from 7 March to 12 April 2022 in the North Atlantic sector of the Arctic, a main gateway of atmospheric transport into and out of the Arctic. Here, we investigate to which degree the meteorological and sea ice conditions during the campaign align with the long-term climatology (1979–2022). For this purpose, we use the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis v5 (ERA5), satellite data, and measurements at Ny-Ålesund, including atmospheric soundings. The observations and reanalysis data revealed two distinct periods with different weather conditions during HALO-(𝒜𝒞)3: the campaign started with a warm period (11–20 March 2022) where strong southerly winds prevailed that caused poleward transport of warm and moist air masses, so-called moist and warm air intrusions (WAIs). Two WAI events were identified as atmospheric rivers (ARs), which are narrow bands of strong moisture transport. These warm and moist air masses caused the highest measured 2 m temperatures (5.5 °C) and daily precipitation rates (42 mm d−1) at Ny-Ålesund for March since the beginning of the record (1993). Over the sea ice northwest of Svalbard, ERA5 indicated record-breaking rainfall. After the passage of a strong cyclone on 21 March 2022, a cold period followed. Northerly winds advected cold air into the Fram Strait, causing marine cold air outbreaks (MCAOs) until the end of the campaign. This second phase included one of the longest MCAO events found in the ERA5 record (19 d). On average, the entire campaign period was warmer than the climatological mean due to the strong influence of the ARs. In the Fram Strait, the sea ice concentration was well within the climatological variability over the entire campaign duration. However, during the warm period, a large polynya opened northeast of Svalbard, untypical for this season. Compared to previous airborne field campaigns focusing on the evolution of (mixed-phase) clouds, a larger variety of MCAO conditions was observed during HALO-(𝒜𝒞)3. In summary, air mass transport into and out of the Arctic was more pronounced than usual, providing exciting prospects for studying air mass transformation using HALO-(𝒜𝒞)3.

Estimating effects of wind and waves on the Doppler centroid frequency shift for the SAR retrieval of ocean currents

Estimating the effects of wind and waves (EWW) on the Doppler centroid frequency shift is a key step in the Synthetic Aperture Radar (SAR) retrieval of ocean currents. However, available models for estimating EWW may exhibit errors as they fail to fully consider the wave and current effects or uncorrected electromagnetic pointing errors. In this study, we corrected the electromagnetic pointing error of Sentinel-1A. In particular, by matching the current data provided by the high-frequency (HF) radar, we accurately obtained the EWW from July 2020 to June 2023. By using the wind and wave parameters provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), the variation characteristics of EWW were analyzed and four geophysical model functions (GMFs) were established using different wave parameters. The application of these GMFs to the retrieval of currents was validated using the HF radar. The optimal current retrieval accuracy in terms of the time series and statistical results was then achieved using the model containing the most comprehensive wave parameters, with a bias of 0.01 m/s and a root mean square error of 0.19 m/s. The proposed model demonstrates the ability to retrieve 100 km scale eddy currents in the Gulf Stream, exhibiting a high agreement with HYbrid Coordinate Ocean Model (HYCOM) reanalysis data, with a bias and root mean square error of approximately −0.03–0.13 m/s and 0.2–0.32 m/s, respectively. The results fully demonstrate that the GMFs established in this study can facilitate the improvement of the SAR retrievals of ocean currents. Moreover, the ability of the proposed model to capture the velocity variations of subscale eddy currents is proved, providing an important tool for oceanographic research.

Climatology of the terms and variables of transformed Eulerian-mean (TEM) equations from multiple reanalyses: MERRA-2, JRA-55, ERA-Interim, and CFSR

Abstract. A 30-year (1980–2010) climatology of the major variables and terms of the transformed Eulerian-mean (TEM) momentum and thermodynamic equations is constructed by using four global atmospheric reanalyses: the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2); the Japanese 55-year Reanalysis (JRA-55); the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim); and the Climate Forecast System Reanalysis (CFSR). Both the reanalysis ensemble mean (REM) and the differences in each reanalysis from the REM are investigated in the latitude–pressure domain for December–January–February and for June–July–August. For the REM investigation, two residual vertical velocities (the original one and one evaluated from residual meridional velocity) and two mass streamfunctions (from meridional and vertical velocities) are compared. Longwave (LW) radiative heating and shortwave (SW) radiative heating are also shown and discussed. For the TEM equations, the residual terms are also calculated and investigated for their potential usefulness, as the residual term for the momentum equation should include the effects of parameterized processes such as gravity waves, while that for the thermodynamic equation should indicate the analysis increment. Inter-reanalysis differences are investigated for the mass streamfunction, LW and SW heating, the two major terms of the TEM momentum equation (the Coriolis term and the Eliassen–Palm flux divergence term), and the two major terms of the TEM thermodynamic equation (the vertical temperature advection term and the total diabatic heating term). The spread among reanalysis TEM momentum balance terms is around 10 % in Northern Hemisphere winter and up to 50 % in Southern Hemisphere winter. The largest uncertainties in the thermodynamic equation (about 50 %) are found in the vertical advection, for which the structure is inconsistent with the differences in heating. The results shown in this paper provide basic information on the degree of agreement among recent reanalyses in the stratosphere and upper troposphere in the TEM framework.

Long-term changes in tropospheric temperature in India: Insights from radiosonde measurements, reanalysis data and CMIP6 model projections

The continuous rise in greenhouse gases (GHGs) in the atmosphere is one of the major factors responsible for the global warming and enhanced tropospheric temperature. In this context, we examine the long-term spatiotemporal changes in the tropospheric temperature (1000–100 hPa) in India using radiosonde measurements and the reanalysis data from European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5), Modern-Era Retrospective analysis for Research and Application Version 2 (MERRA2) and National Center for Environmental Prediction (NCEP) for the period 1980–2020. We also investigate the future projections of tropospheric temperature in the Shared Socio-economic Pathways (SSPs) 2–4.5 and 5–8.5 by using the Coupled Model Intercomparison Project 6 (CMIP6) results for the period 2015–2100 over India. Our analysis shows a significant positive trend (0.01–0.04 °C/yr) in the annual mean temperature in the upper troposphere (500–100 hPa) in India during the period 1980–2020. The warming trends are also noticeable in the middle and lower troposphere of India. Among the regions, the northeast, north central and northwest India exhibit significant warming of about 0.01–0.06 °C/yr in the upper troposphere (500–100 hPa). On the other hand, most regions show a decline in temperature at the tropopause (100 hPa), which is stronger in the interior peninsular, north central and east coast of India, at about −0.05 ± 0.01°C/yr. All reanalysis data show consistent warming and cooling trends in the respective regions as observed from the radiosondes, albeit slight differences in their values. The future high emission scenario of SSP5–8.5 shows a warming trend of about 0.04–0.08 °C/yr at 1000–200 hPa by the end of the century, which is highest at 250 hPa. The increasing trend in tropospheric temperature is a serious concern, which calls for adaptation and mitigation measures to alleviate the impacts of this accelerated warming in India.

A machine learning-based framework for spatio-temporal extension and filling of SMOS surface soil moisture observations over Canada

ABSTRACT Advancements in global satellite missions have revolutionized the assessment of Surface Soil Moisture (SSM) at global to local scales. However, spatio-temporal data discontinuities in specific regions remain a challenge. This study proposes a Machine Learning (ML)-based framework to extend the Soil Moisture Ocean Salinity (SMOS) SSM product both in the spatial and temporal domains, over Canada. In the first phase of the proposed framework, ML models based on Random Forest (RF) and Convolutional Neural Networks (CNN) are trained and validated with SMOS SSM as target and SSM-relevant climatic variables and geophysical variables, obtained from fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data (ERA5), for the 2011–2020 period, as predictors. Developed models, when tested on unseen data for the years 2021–2022, suggest slightly better performance of the RF model compared to CNN, with root mean square error (RMSE) of 0.033 and 0.056 respectively; prediction biases mostly noted for regions with large inter-annual variability. The spatial filling of SSM for grid cells that were excluded during the training process, with similar land types as those in the SMOS training data, yields reasonable performance, with RF (RMSE = 0.013) performing better than CNN (RMSE = 0.064). In the second phase, the RF model is selected to extend the SMOS dataset for the 2008–2010 period. The temporal correlation between extended SMOS and ASCAT (Advanced Scatterometer) SSM demonstrates a reasonable association, with correlation coefficient exceeding 0.6. Additionally, spatial correlation analysis reveals similar patterns between the two datasets, with smaller values for the summer season owing to the importance of local processes on SSM during this period. However, extending SMOS SSM spatially for surface types that were not included in the training process such as peatlands, remains a challenge warranting additional studies. The developed framework is robust and can address spatio-temporal discontinuities in other SSM products.

Investigating the medium‐range predictability of European heatwave onsets in relation to weather regimes using ensemble reforecasts

AbstractIn this study, the medium‐range predictability of heatwave (HW) onsets in four midlatitude European regions is investigated statistically with the help of ensemble reforecasts for the period 2001–2018. The concept of Euro‐Atlantic weather regimes is adopted to characterise HWs (about 50 in each region) and to study whether forecast skill may depend on the large‐scale dynamical setup. HW onsets over the British Isles and Scandinavia are mainly associated with Scandinavian and European blocking regimes, whereas the “no regime” case is observed more frequently for Central Europe. Stratified by weather regime, the predictability of heatwave onsets is then studied by means of a multiple metric‐based analysis of European Centre for Medium‐Range Weather Forecasts (ECMWF) and Global Ensemble Forecast System Version 12 (GEFSv12) ensemble reforecasts. For two of the regions considered, Central Europe and the British Isles, a conclusive picture is obtained: medium‐range predictive skill is significantly higher for HW onsets associated with Scandinavian or European blocking compared with cases with no pronounced regime. This skill advantage mostly concerns the large‐scale flow and, to some extent, 850‐hPa temperatures, but is generally not reflected in the correct prediction of near‐surface temperatures. Finally, we investigate for two regions how exceptionally good or poor forecasts are related to the atmospheric state during or shortly after forecast initialisation. At 10 days lead time, poor large‐scale flow predictive skill for Central European HW onsets is linked to anomalously high baroclinicity further upstream and an intensified North Atlantic jet stream, whereas good forecasts on average feature an initial state close to climatology. Forecast skill for near‐surface temperatures is not affected by such dynamical precursors, but rather by pre‐existing soil‐moisture anomalies. For the British region, exceptionally good forecasts of both large‐scale flow and near‐surface temperatures are associated with an already established continental blocking. In contrast to Central Europe, pre‐existing soil‐moisture anomalies play less of a role there.

Impact of ocean in-situ observations on ECMWF sub-seasonal forecasts

We assess for the first time the impact of in-situ ocean observations on European Centre for Medium-Range Weather Forecasts (ECMWF) sub-seasonal forecasts of both ocean and atmospheric conditions. A series of coupled reforecasts have been conducted for the period 1993-2015, in which different sets of ocean observations were withdrawn in the production of the ocean initial conditions. Removal of all ocean in-situ observations in the initial conditions leads to significant degradation in the forecasts of ocean surface and subsurface mean state at lead times from week 1 to week 4. The negative impact is predominantly caused by the removal of the Argo observing system in recent decades. Changes in the mean state of atmospheric variables are comparatively small but significant in the forecasts of lower and upper atmospheric circulation over large regions. Our results highlight the value of continuous, real-time in-situ observations of the surface and subsurface ocean for coupled forecasts in the sub-seasonal range.