European Centre For Medium-Range Weather Forecasts Research Articles

Feasibility of Wave Simulation in Typhoon Using WAVEWATCH-III Forced by Remote-Sensed Wind

The purpose of our work was to assess the feasibility of hindcasting waves using WAVEWATCH-III (WW3) in a typhoon by assembling winds from multiple remote-sensed products. During the typhoon season in 2021–2022, the swath wind products in the Western Pacific Ocean were collected from scatterometers and radiometers. Cyclonic winds with a spatial resolution of 0.125° at intervals of 6 h were obtained by assembling the remote-sensed winds from those satellites. The maximum wind speeds, Vmax, were verified using the reanalysis data from the National Hurricane Center (NHC), yielding a root-mean-squared error (RMSE) of 4.79 m/s and a scatter index (SI) value of 0.2. The simulated wave spectrum was compared with the measurements from Surface Waves Investigation and Monitoring (SWIM) carried out on the Chinese–French Oceanography Satellite (CFOSAT), yielding a correlation coefficient (Cor) of 0.80, squared error (Err) of 0.49, RMSE of significant wave height (SWH) of 0.48 m with an SI of 0.25, and an RMSE of the peak wave period (PWP) of 0.95 s with an SI of 0.10. The bias of wave (WW3 minus European Centre for Medium-Range Weather Forecasts (ECMWFs) reanalysis (ERA-5)) concerning the bias of wind (assembling minus ERA-5) showed that the WW3-simulated SWH with the assembling wind forcing was significantly higher than that with the ERA-5 wind forcing. Moreover, the bias of SWH gradually increased with an increasing bias of wind speed; i.e., the bias of SWH increased up to 4 m as the bias of wind speed reached 30 m/s. It was concluded that the assembling wind from multiple scatterometers and radiometers is a promising source for wave simulations via WW3 in typhoons.

Quantifying stratospheric ozone trends over 1984–2020: a comparison of ordinary and regularized multivariate regression models

Abstract. Accurate quantification of long-term trends in stratospheric ozone can be challenging due to their sensitivity to natural variability, the quality of the observational datasets, and non-linear changes in forcing processes as well as the statistical methodologies. Multivariate linear regression (MLR) is the most commonly used tool for ozone trend analysis; however, the complex coupling in many atmospheric processes can make it prone to the issue of over-fitting when using the conventional ordinary-least-squares (OLS) approach. To overcome this issue, here we adopt a regularized (ridge) regression method to estimate ozone trends and quantify the influence of individual processes. We use the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) merged dataset (v2.7) to derive stratospheric ozone profile trends for the period 1984–2020. Besides SWOOSH, we also analyse a machine-learning-based satellite-corrected gap-free global stratospheric ozone profile dataset from a chemical transport model (ML-TOMCAT) and output from a chemical transport model (TOMCAT) simulation forced with European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis. For 1984–1997, we observe smaller negative trends in the SWOOSH stratospheric ozone profile using ridge regression compared to OLS. Except for the tropical lower stratosphere, the largest differences arise in the mid-latitude lowermost stratosphere (>4 % per decade difference at 100 hPa). From 1998 and the onset of ozone recovery in the upper stratosphere, the positive trends estimated using the ridge regression model (∼1 % per decade near 2 hPa) are smaller than those using OLS (∼2 % per decade). In the lower stratosphere, post-1998 negative trends with large uncertainties are observed and ridge-based trend estimates are somewhat smaller and less variable in magnitude compared to the OLS regression. Aside from the tropical lower stratosphere, the largest difference is around 2 % per decade at 100 hPa (with ∼3 % per decade uncertainties for individual trends) in northern mid-latitudes. For both time periods the SWOOSH data produce large negative trends in the tropical lower stratosphere with a correspondingly large difference between the two trend methods. In both cases the ridge method produces a smaller trend. The regression coefficients from both OLS and ridge models, which represent ozone variations associated with natural processes (e.g. the quasi-biennial oscillation, solar variability, El Niño–Southern Oscillation, Arctic Oscillation, Antarctic Oscillation, and Eliassen–Palm flux), highlight the dominance of dynamical processes in controlling lower-stratospheric ozone concentrations. Ridge regression generally yields smaller regression coefficients due to correlated explanatory variables, and care must be exercised when comparing fit coefficients and their statistical significance across different regression methods. Comparing the ML-TOMCAT-based trend estimates with the ERA5-forced model simulation, we find ML-TOMCAT shows significant improvements with much better consistency with the SWOOSH dataset, despite the ML-TOMCAT training period overlapping with SWOOSH only for the Microwave Limb Sounder (MLS) measurement period. The largest inconsistencies with respect to SWOOSH-based trends post-1998 appear in the lower stratosphere where the ERA5-forced model simulation shows positive trends for both the tropics and the mid-latitudes. The large differences between satellite-based data and the ERA5-forced model simulation confirm significant uncertainties in ozone trend estimates, especially in the lower stratosphere, underscoring the need for caution when interpreting results obtained with different regression methods and datasets.

Application of deep learning algorithms to correct bias in CMIP6 simulations of surface air temperature over the Indian monsoon core region

AbstractIndian subcontinent witnessed a rise in surface air temperature (SAT) in recent decades, during the summer months of March, April and May. The monsoon core region (MCR) of India experiences a hot and humid climate, with temperatures typically highest in May and June before the onset of the monsoon. Global climate model (GCM) simulations of SAT are very much essential to understand the future climate of Indian MCR. Biases in GCMs simulations are due to insufficient knowledge of parameterizations and various assumptions that are made to simulate the complex interactions between land, ocean and atmosphere. The objective of this study is to correct the bias in the Coupled Model Intercomparison Project Phase 6 (CMIP6)–GCM simulations of SAT during March, April and May months over MCR for the historical period 1985–2014 and shared socio‐economic pathways (SSPs) SSP2‐4.5 and SSP5‐8.5 for the period 2015–2100. SAT dataset of fifth‐generation reanalysis (ERA5) of the European Centre for Medium‐Range Weather Forecasts (ECMWF) is used as reference dataset to perform bias correction for the historical period. Preliminary investigation of both SAT datasets has shown that there exists considerable warm bias (1.47°C) over the MCR. Bias correction is performed using a one‐dimensional convolutional neural network (CNN‐1D) and a convolutional long short‐term memory network (CNN‐LSTM) deep learning algorithm. The performance of these algorithms is evaluated with the statistical metrics such as root‐mean‐square error (RMSE), normalized root‐mean‐square error, Nash–Sutcliffe efficiency, mean absolute error, percent bias, correlation coefficient and dynamic time warping. RMSE and percent bias were decreased to 0.35°C and 0.8% with CNN‐LSTM algorithm. The CNN‐LSTM algorithm also preserves the year‐to‐year variability of SAT. Hence, CNN‐LSTM algorithm is found to be suitable for the bias correction of GCM simulations of SAT with encouraging results.

Snow Cover Response to Climatological Factors at the Beas River Basin of W. Himalayas from MODIS and ERA5 Datasets.

Glaciers and snow are critical components of the hydrological cycle in the Himalayan region, and they play a vital role in river runoff. Therefore, it is crucial to monitor the glaciers and snow cover on a spatiotemporal basis to better understand the changes in their dynamics and their impact on river runoff. A significant amount of data is necessary to comprehend the dynamics of snow. Yet, the absence of weather stations in inaccessible locations and high elevation present multiple challenges for researchers through field surveys. However, the advancements made in remote sensing have become an effective tool for studying snow. In this article, the snow cover area (SCA) was analysed over the Beas River basin, Western Himalayas for the period 2003 to 2018. Moreover, its sensitivity towards temperature and precipitation was also analysed. To perform the analysis, two datasets, i.e., MODIS-based MOYDGL06 products for SCA estimation and the European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis of the Global Climate (ERA5) for climate data were utilized. Results showed an average SCA of ~56% of its total area, with the highest annual SCA recorded in 2014 at ~61.84%. Conversely, the lowest annual SCA occurred in 2016, reaching ~49.2%. Notably, fluctuations in SCA are highly influenced by temperature, as evidenced by the strong connection between annual and seasonal SCA and temperature. The present study findings can have significant applications in fields such as water resource management, climate studies, and disaster management.

Seasonal climate models for national wheat yield forecasts in Brazil

National wheat yield depends on climate conditions and usually remains unknown until harvest. In-season knowledge can be provided by wheat yield forecast systems, supporting the decision-making of farmers, food traders, or policymakers. In this study, we improved a previously developed statistical wheat yield model to forecast trend-corrected wheat yield in Brazil with monthly temperature and precipitation data from seasonal climate models (SCM) from the last three months before harvest. We chose SCM from the European Center for Medium-Range Weather Forecasts (ECMWF), the National Centers for Environmental Prediction (NCEP), and the UK-based Met-Office (UKMO). A multi-model ensembles (MME) approach from the three individual models as well as a climatology (CLIMATE) approach were also tested. Wheat yield forecasts were issued at the beginning of each month from planting in April to harvest in November. Each month, features from future months are forecasted by SCM, and past features are supplemented with observations from weather stations. Our approach shows a 12% RMSE in forecasting yield early in the season, from April to June. Forecasts start to improve from July onwards, with shorter lead times and including observed features from September onwards. At the beginning of October, about two months before harvest is completed, wheat yield can be forecasted with 7.6%, 7.9%, 7.9%, 9.1%, and 9.3% RMSE using climate data from UKMO, ECMWF, MME, NCEP, and CLIMATE respectively. Seasonal climate models can be useful tools to forecast national wheat yield, even shortly before harvest to prepare for possible food shortages. Our approach could be applied to other staple crops and regions.

Quantification of Amu River Riverbank Erosion in Balkh Province of Afghanistan during 2004–2020

In this study, we propose quantifying the Amu River riverbank erosion with the modelled river discharge in Kaldar District, Balkh Province of Afghanistan from 2004 to 2020. We propose a framework synergizing multi-source information for modelling the erosion area based on three components: (1) river discharge, (2) river width, and (3) erosion area. The total river discharge for the watershed shared by Afghanistan and Tajikistan was modelled using hydrological parameters from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) data through multivariate linear regression with ground station data. The river width was determined manually using the Normalized Difference Water Index (NDWI) derived from Landsat data. The riverbank erosion area was derived from the digital shoreline analysis using the NDWI. The digital shoreline analysis showed that, between 2008 and 2020, the average riverbank erosion area in Kaldar District is about 5.4 km2 per year, and, overall, 86.3 km2 during 2004–2020 due to flood events. The significantly higher land loss events occurred at 10 km2 bank erosion during the years 2008–2009 and 2015–2016, and 19 km2 peak erosion occurred during 2011–2012. A linear relation between the erosion area with respect to the discharge intensity and the specific stream power was observed with an R2 of 0.84 and RMSE of 1.761 for both.

Correction for the Attenuation Due to Atmospheric Gas and Stratiform Clouds in Triple-Frequency Radar Observations of the Microphysical Properties of Snowfall

For triple-frequency radar, the attenuation attributed to atmospheric gases and stratiform clouds is diverse due to different snowfall microphysical properties, particularly in regions far from the radar. When using triple-frequency ground-based radar measurements, evaluating the attenuation of the three radars at different heights is common to derive attenuation-corrected effective reflectivity. Therefore, this study proposes a novel quality-controlled approach to identify radar attenuation due to gases and stratiform clouds that can be neglected due to varying snowfall microphysical properties and assess attenuation along the radar observation path. The key issue lies in the lack of information about vertical hydrometeor and cloud distribution. Therefore, European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data are employed. The Self-Similar-Rayleigh-Gans Approximation (SSRGA) for the nonspherical scattering model in the Passive and Active Microwave TRAnsfer model 2 (PAMTRA2) is compared and analyzed against other scattering models to obtain the optimal triple-frequency radar attenuation correction strategies for stratiform cloud meteorological conditions with varying snowfall microphysical properties. This methodology paves the way for understanding differential attenuation attributed to gas and stratiform clouds with snowfall microphysical properties. Simultaneously, the bin-by-bin approximation method is used to perform the attenuation correction. The two-way attenuation correction increased up to 4.71 dB for heights above 6 km, remaining minimal for regions with heights below 6 km. These values, attributable to gases and stratiform clouds’ two-way attenuation, are nonnegligible, especially at distances far from the W-band radar at heights above 6 km. Both values are relatively small for the X- and Ka-band radars and can be neglected for the varying snowfall microphysical properties. The attenuation correction of triple-frequency radar reflectivity is validated using the cross-calibration and dual-frequency reflectivity ratios. The results show that the method is valid and feasible.

Using the Commercial GNSS RO Spire Data in the Neutral Atmosphere for Climate and Weather Prediction Studies

Recently, the NOAA has included GNSS (Global Navigation Satellite System) Radio Occultation (RO) data as one of the crucial long-term observables for weather and climate applications. To include more GNSS RO data in its numerical weather prediction systems, the NOAA Commercial Weather Data Pilot program (CWDP) started to explore the commercial RO data available on the market. After two rounds of pilot studies, the CWDP decided to award the first Indefinite Delivery Indefinite Quantity (IDIQ) contract to GeoOptics and Spire Incs. in 2020. This study examines the quality of Spire RO data products for weather and climate applications. Spire RO data collected from commercial CubeSats are carefully compared with data from Formosa Satellite Mission 7–Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2), the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5), and high-quality radiosonde data. The results demonstrate that, despite their generally lower Signal-Noise-Ratio (SNR), Spire RO data show a pattern of lowest penetration height similar to that of COSMIC-2. The Spire and COSMIC-2 penetration heights are between 0.6 and 0.8 km altitude over tropical oceans. Although using different GNSS RO receivers, the precision of Spire STRATOS receivers is of the same quality as those of the COSMIC-2 TriG (Global Positioning System—GPS, GALILEO, and GLObal NAvigation Satellite System—GLONASS) RO Receiver System (TGRS) receivers. Furthermore, the Spire and COSMIC-2 retrieval accuracies are quite comparable. We validate the Spire temperature and water vapor profiles by comparing them with collocated radiosonde observation (RAOB) data. Generally, over the height region between 8 km and 16.5 km, the Spire temperature profiles match those from RS41 RAOB very well, with temperature biases of <0.02 K. Over the height range from 17.8 to 26.4 km, the temperature differences are ~−0.034 K, with RS41 RAOB being warmer. We also estimate the error covariance matrix for Spire, COSMIC-2, and KOMPSAT-5. The results show that the COSMIC-2 estimated error covariance values are slightly more significant than those from Spire over the oceans at the mid-latitudes (45°N–30°N and 30°S–45°S), which may be owing to COSMIC-2 SNR being relatively lower at those latitudinal zones.

Machine Learning-Based Calibrated Model for Forecast Vienna Mapping Function 3 Zenith Wet Delay

An accurate estimation of zenith wet delay (ZWD) is crucial for global navigation satellite system (GNSS) positioning and GNSS-based precipitable water vapor (PWV) inversion. The forecast Vienna Mapping Function 3 (VMF3-FC) is a forecast product provided by the Vienna Mapping Functions (VMF) data server based on the European Centre for Medium-Range Weather Forecasts (ECMWF)-based numerical weather prediction (NWP) model. The VMF3-FC can provide ZWD at any time and for any location worldwide; however, it has an uneven accuracy distribution and fails to match the application requirements in certain areas. To address this issue, in this study, a calibrated model for VMF3-FC ZWD, named the XZWD model, was developed by utilizing observation data from 492 radiosonde sites globally from 2019–2021 and the eXtreme Gradient Boosting (XGBoost) algorithm. The performance of the XZWD model was validated using 2022 observation data from the 492 radiosonde sites. The XZWD model yields a mean bias of −0.03 cm and a root-mean-square error (RMSE) of 1.64 cm. The XZWD model outperforms the global pressure and temperature 3 (GPT3) model, reducing the bias and RMSE by 94.64% and 58.90%, respectively. Meanwhile, the XZWD model outperforms VMF3-FC, with a reduction of 92.68% and 6.29% in bias and RMSE, respectively. Furthermore, the XZWD model reduces the impact of ZWD accuracy by latitude, height, and seasonal variations more effectively than the GPT3 model and VMF3-FC. Therefore, the XZWD model yields higher stability and accuracy in global ZWD forecasting.

3D wind field profiles from hyperspectral sounders: revisiting optic-flow from a meteorological perspective

In this work, we present an efficient optic flow algorithm for the extraction of vertically resolved 3D atmospheric motion vector (AMV) fields from incomplete hyperspectral image data measures by infrared sounders. The model at the heart of the energy to be minimized is consistent with atmospheric dynamics, incorporating ingredients of thermodynamics, hydrostatic equilibrium and statistical turbulence. Modern optimization techniques are deployed to design a low-complexity solver for the energy minimization problem, which is non-convex, non-differentiable, high-dimensional and subject to physical constraints. In particular, taking advantage of the alternate direction of multipliers methods (ADMM), we show how to split the original high-dimensional problem into a recursion involving a set of standard and tractable optic-flow sub-problems. By comparing with the ground truth provided by the operational numerical simulation of the European Centre for Medium-Range Weather Forecasts (ECMWF), we show that the performance of the proposed method is superior to state-of-the-art optical flow algorithms in the context of real infrared atmospheric sounding interferometer (IASI) observations.

Euro-Atlantic Weather Regimes and Their Modulation by Tropospheric and Stratospheric Teleconnection Pathways in ECMWF Reforecasts

Abstract This study combines operational reforecasts (2001–21) with results from a lower-resolution 41-yr reforecast (1980–2020) to provide a robust assessment of wintertime Euro-Atlantic regimes and their modulation by tropospheric and stratospheric teleconnection pathways in the European Centre for Medium-Range Weather Forecasts (ECMWF) Subseasonal to Seasonal Prediction project (S2S). In both operational and lower-resolution reforecasts, the climatological properties of wintertime Euro-Atlantic regimes, including regime structures, frequencies, and transition probabilities, are accurately simulated at S2S lead times. However, the 41-yr reforecasts allow us to diagnose substantial errors in regime statistics when conditioned on modes of intraseasonal-to-interannual variability. In particular, ECMWF reforecasts underestimate the response of the North Atlantic Oscillation (NAO) to the Madden–Julian oscillation (MJO) and fail to reproduce the modulation of MJO–NAO teleconnections by El Niño–Southern Oscillation (ENSO). Teleconnection and atmospheric wave diagnostics highlight two specific issues that are likely to contribute to these conditional errors in ECMWF reforecasts: (i) insufficient propagation of Rossby wave activity from the Pacific to the Atlantic following MJO phase 3 during El Niño conditions, when the direct tropospheric teleconnection pathway is most active; and (ii) an underestimated response of the stratospheric polar vortex following MJO phase 8 during La Niña conditions, when the indirect stratospheric teleconnection pathway is most active. Improving the representation of tropospheric and stratospheric teleconnection pathways is thus a priority for improving ECMWF forecasts of extratropical weather regimes and their associated surface impacts. Significance Statement Subseasonal to Seasonal Prediction project (S2S) forecasts are used operationally at ECMWF to provide early warning of cold conditions in Europe associated with persistent large-scale circulation patterns known as weather regimes. On average, ECMWF reforecasts accurately simulate wintertime Euro-Atlantic regime structures and frequencies at S2S lead times. However, regime forecasts show substantial errors when we restrict our analysis to certain phases of intraseasonal and interannual variability, such as El Niño–Southern Oscillation (ENSO). These errors are related to deficiencies in the simulated response of weather regimes to well-predicted variability in the tropics. Improving the representation of such tropical–extratropical teleconnections will improve predictions of extratropical weather regimes and their associated surface impacts.

Intercomparison of tropospheric and stratospheric mesoscale kinetic energy resolved by the high‐resolution global reanalysis datasets

AbstractWith the development of advanced data assimilation and computing techniques, many modern global reanalysis datasets aim to resolve the atmospheric mesoscale spectrum. However, large uncertainties remain with respect to the representation of mesoscale motions in these reanalysis datasets, for which a clear understanding is lacking. The aforementioned challenges have served as a strong motivation to reveal and quantify their mesoscale differences. This study presents the first comprehensive global intercomparison of the tropospheric and stratospheric mesoscale kinetic energy and its spectra over two selected periods of summer and winter events among six leading high‐resolution atmospheric reanalysis products: European Centre for Medium‐Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5), China Meteorological Administration Reanalysis, Modern‐Era Retrospective Analysis for Research and Applications version 2, National Centers for Environmental Prediction's Climate Forecast System version 2 (CFSv2), Japanese 55‐year Reanalysis, and ECMWF Reanalysis‐Interim. A state‐of‐the‐art global operational model is adopted as a supplementary reference. Although all reanalysis datasets can reproduce broad distribution characteristics that are grossly consistent with the 9 km model, there are substantial discrepancies among them in magnitudes. The ability to capture mesoscale signals is closely linked to their resolutions, but it is also impacted by other factors, including, but not limited to, the selected types of energy, seasons, altitudes, latitudes, model diffusions, parametrization schemes, moist condition, assimilation methods, and observation inputs. Moreover, all datasets illustrate conclusive behaviors for the prevalence of the rotational component in the troposphere, whereas only very few products fail to exhibit the dominance of the divergent component in the stratosphere. Overall, stratospheric ERA5 and CFSv2 outperform the other reanalysis datasets, and only these two can reproduce the feature of the canonical kinetic energy spectrum with a distinct shift from a steeper slope (approximately −3) at lower wave numbers to a shallower slope (approximately −5/3) at higher wave numbers. In addition, the relative disparities among datasets increase dramatically with height, and they are more pronounced in the divergent component. It is also found that the correlations among these datasets are much weaker in the Tropics.

Convective weather event monitoring with multispectral image analysis of INSAT-3D/3DR over Indian domain

Pre-monsoon season (March to May) is very challenging as convective activities prevails almost throughout the country. Most of the Rabi crops harvesting affected and sometimes suffer great losses due to sudden rain or high winds. INSAT-3D/3DR satellite images and derived products provides continuous support to the forecasters and end users in monitoring such events and thereafter significant value addition improves the prediction. This information was found to be very useful where actual ground based or upper air observations are limited or especially over data sparse or difficult terrain regions. In this work, we have examined three weather events at different Geographical locations (i) Rainfall over Bihar-24-26 June, 2020 (ii) Delhi & NCR region on 17 June, 2022 (iii) NE region activity in 16-18 June, 2022. The Real Time Analysis of Products and Information Dissemination (RAPID) web based tool was utilized in monitoring and diagnosing the convective weather events based on the brightness temperature & derived products like Outgoing longwave radiation, upper tropospheric humidity, insolation etc & RGB imagery composite in terms of day & night time microphysics daily operational products. The time series of the wind derived products for Delhi NCR rainfall and NE rainfall products also generated through RAPID. The synoptic model analysis provides valuable inputs for these mesoscale convective weather events. The southerly wind flow (at 925 hPa) and velocity convergence (at 500 hPa) analysis of European Centre for Medium Range Weather Forecasting (ECMWF) supports the severity of NE event occurred on 16-18 June, 2022. Therefore, utilization of near real time INSAT-3D/3DR products along with appropriate synoptic model analysis can help the forecasters to understand better about such mesoscale convective events & accurate forecast with sufficient lead time can save the life and property.

Sources of predictability of synoptic‐scale rainfall during the West African summer monsoon

AbstractNumerical‐model‐based forecasts of precipitation exhibit poor skill over northern tropical Africa when compared with climatology‐based forecasts and with other tropical regions. However, as recently demonstrated, purely data‐driven forecasts based on spatio‐temporal dependences inferred from gridded satellite rainfall estimates show promise for the prediction of the 24‐hr precipitation occurrence rate in this region. The present work explores this potential further by advancing the statistical model and providing meteorological interpretations of the performance results. Advances include (a) the use of a recently developed correlation metric, the Coefficient of Predictive Ability (CPA), to identify predictors, (b) forecast evaluation with robust reliability diagrams and score decompositions, (c) a study domain over tropical Africa nested in a considerably enlarged spatio‐temporal domain to identify coherent propagating features, and (d) the introduction of a novel coherent‐linear‐propagation factor to quantify the coherence of propagating signals. The statistical forecast is compared with a climatology‐based benchmark, the European Centre for Medium‐Range Weather Forecasts (ECMWF) operational ensemble forecast, and a statistically postprocessed ensemble forecast. All methods show poor skill within the main rainbelt over northern tropical Africa, where differences in Brier scores between the different approaches are hardly statistically significant. However, the data‐driven forecast outperforms the other methods along the fringes of the rainbelt, where meridional rainfall gradients are large. The coherent‐linear‐propagation factor, in concert with metrics of convective available potential energy and convective instability, reveals that high stochasticity in the rainbelt limits predictability. At the fringes of the rainbelt, the data‐driven approach leverages coherent precipitation features associated with propagating tropical weather systems such as African Easterly Waves.

Performance of land surface schemes on simulation of land falling tropical cyclones over Bay of Bengal using ARW model

The present study encompasses the performance of Land Surface Model (LSM) physics on simulation of Tropical Cyclones (TCs) key characteristics - track, mean sea level pressure (MSLP), maximum sustained wind (MSW) and rainfall. The impact of four LSM schemes - Thermal Diffusion, Noah, RUC and Noah-MP, is evaluated for the simulation of Severe Cyclonic Storm (SCS) ‘Vardah’ that crossed Tamil Nadu coast, near Chennai on 12 December, 2016 and Extremely Severe Cyclonic Storms (ESCS) ‘Fani’ that crossed Odisha coast, close to Puri on 03 May, 2019. For this purpose, the Advanced Weather Research and Forecasting (ARW) model, configured with a single domain of 9 km horizontal resolution covering the Bay of Bengalis considered. The initial and lateral boundary conditions to the model integration are taken from National Centers for Environmental Prediction (NCEP) Final Analysis (FNL). The model simulated track is verified with India Meteorological Department (IMD) observed track for both the cases. The model simulated MSW and MSLP at the landfall location is validated with IMD best estimation along with fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) Re-analysis (ERA5) products. The rainfall associated with both the cyclones are compared with ERA5 and Global Precipitation Measurement (GPM) rainfall for its validation. The track of TCs Vardah and Fani are well simulated with all the four land surface schemes with reasonable accuracy in landfall position and time of landfall of the systems. The Along Track Error (ATE) and Cross Track Error (CTE) are minimal for the unified Noah LSM scheme. The landfall position error (about 2 km only) is significantly improved with the unified Noah scheme. In case of rainfall forecast, LSMs tend to overestimate the rainfall during landfall of both systems. It is also noticed that overestimation is more towards inland than on the coast. Out of all four LSMs, rainfall estimation from the RUC is closest to the GPM and ERA5 rainfall estimates during landfall. In addition to this, RUC scheme intensifies the cyclones in terms of MSLP and MSW during the landfall of the system as compared to the other parameterization schemes.

The role of in situ ocean data assimilation in ECMWF subseasonal forecasts of sea‐surface temperature and mixed‐layer depth over the tropical Pacific ocean

AbstractThe tropical Pacific plays an important role in modulating the global climate through its prevailing sea‐surface temperature spatial structure and dominant climate modes like El Niño–Southern Oscillation (ENSO), the Madden–Julian Oscillation (MJO), and their teleconnections. These modes of variability, including their oceanic anomalies, are considered to provide sources of prediction skill on subseasonal timescales in the Tropics. Therefore, this study aims to examine how assimilating in situ ocean observations influences the initial ocean sea‐surface temperature (SST) and mixed‐layer depth (MLD) and their subseasonal forecasts. We analyze two subseasonal forecast systems generated with the European Centre for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS), where the ocean states were initialized using two Observing‐System Experiment (OSE) reanalyses. We find that the SST differences between forecasts with and without ocean data assimilation grow with time, resulting in a reduced cold‐tongue bias when assimilating ocean observations. Two mechanisms related to air–sea coupling are considered to contribute to this growth of SST differences. One is a positive feedback between the zonal SST gradient, pressure gradient, and surface wind. The other is the difference in Ekman suction and mixing at the Equator due to surface wind‐speed differences. While the initial mixed‐layer depth (MLD) can be improved through ocean data assimilation, this improvement is not maintained in the forecasts. Instead, the MLD in both experiments shoals rapidly at the beginning of the forecast. These results emphasize how initialization and model biases influence air–sea interaction and the accuracy of subseasonal forecasts in the tropical Pacific.

Coherent Bimodal Events in Ensemble Forecasts of 2-m Temperature

Abstract A previous study has shown that a large portion of subseasonal-to-seasonal European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecasts for 2-m temperature exhibit properties of univariate bimodality, in some locations occurring in over 30% of forecasts. This study introduces a novel methodology to identify “bimodal events,” meteorological events that trigger the development of spatially and temporally correlated bimodality in forecasts. Understanding such events not only provides insight into the dynamics of the meteorological phenomena causing bimodal events, but also indicates when Gaussian interpretations of forecasts are detrimental. The methodology that is developed allows one to systematically characterize the spatial and temporal scales of the derived bimodal events, and thus uncover the flow states that lead to them. Three distinct regions that exhibit high occurrence rates of bimodality are studied: one in South America, one in the Southern Ocean, and one in the North Atlantic. It is found that bimodal events in each region appear to be triggered by synoptic processes interacting with geographically specific processes: in South America, bimodality is often related to Andes blocking events; in the Southern Ocean, bimodality is often related to an atmospheric Rossby wave interacting with sea ice; and in the North Atlantic, bimodality is often connected to the displacement of a persistent subtropical high. This common pattern of large-scale circulation anomalies interacting with local boundary conditions suggests that any deeper dynamical understanding of these events should incorporate such interactions. Significance Statement Repeatedly running weather forecasts with slightly different initial conditions provides some information on the confidence of a forecast. Occasionally, these sets of forecasts spread into two distinct groups or modes, making the “typical” interpretation of confidence inappropriate. What leads to such a behavior has yet to be fully understood. This study contributes to our understanding of this process by presenting a methodology that identifies coherent bimodal events in forecasts of near-surface air temperature. Applying this methodology to a database of such forecasts reveals several key dynamical features that can lead to bimodal events. Exploring and understanding these features is crucial for saving forecasters’ resources, creating more skillful forecasts for the public, and improving our understanding of the weather.

Effect of Urban Heat Island on Growth Rate of the Convective Boundary Layer Over Baghdad City

Due to human activities and economic growth, in the last decades, urban expansion has increased, and it has a large role in the formation of urban heat island. Urban heat island effects natural physical properties of the air. In this study, data from satellite images included Landsat5 Landsat 9 and reanalysis data from European Centre for Medium-Range Weather Forecasts (ECMWF) by ERA5 model are implemented. ERA5 archives NetCDF file data for many hourly atmospheric elements such as temperature, wind components, sensible heat flux, boundary layer height …etc. All the files were processed by MATLAB, while satellite images processed by ArcGIS to calculate the spectral index such as Normalized difference built up index (NDBI) and supervised classification. Results of composited images and supervised classification show that there is an increase in built up area of about 23% for the periods from July-1994 to July-2022. This change in LULC will create urban heat island for Baghdad and affect heat budget near the surface that will change structure of urban boundary layer, and convective boundary layer (CBL). Near the surface there is an increase in growth rate of convective boundary layer about 0.25m/s at 7AM for July-2022, relative to 0.136 m/s for July-1994. This difference in growth rate is due to urban heat island creation and will increase CBL to about 620m in 2022 relative to 1994 at 10AM.

Analysis of major cyclone storms ‘mocha 2023’, ‘Mandous 2022’ of recent years over India

The current study outlines how ocean conditions contributed to the development, intensification and dissipation of the extremely severe cyclonic storms “Mocha” (ESCS) (2023), severe cyclonic storm (SCS) “Mandous” (2022), very severe cyclonic storm (VSCS) “Asani” (2022), and (ESCS) “Tauktae” (2021). ‘Mocha’ tropical cyclones (TC) formed over warm waters over the southeast Bay of Bengal and the adjacent northern Andaman Sea on May 11–12, 2023, ‘Mandous’ over the southeast Bay of Bengal on December 6-7, 2022. The European Centre for Medium-Range Weather Forecasts (ECMWF) generated ECMWF reanalysis V5 (ERA5), which is used in the present study to analyse the sea surface temperature (SST), latent heat flux, relative vorticity (RV), specific humidity, and relative humidity during the lifetime of the storm. The analysis of the TC’s sea surface temperature data from satellites suggests that a much warmer SST was present throughout the cyclone’s existence, which may have been the primary reason for the TC’s fast intensification. Latent Heat flow (LHF) was found to be high along with SST values. As the TC approached the coast, an RV value that was both positive and significant was discovered. During intensification, Specific and Relative Humidity also had high levels.

The Difference in Cloud Water Resources and Precipitation on the Eastern and Western Sides of the Liupan Mountains Caused by Topographic Effects

In order to explore the possible impact of topography on precipitation between the eastern and western sides of the Liupan Mountains (LMs) in the northwest region of China, the differences in distribution characteristics of total column water vapor (TCWV), total column cloud water (TCCW), and total precipitation (TP) were studied by using the 40-year hourly data of the fifth generation reanalysis (ERA5) from the European Centre for Medium-Range Weather Forecasts (ECMWF). The results showed the following: (1) The TCCW and TP on the eastern and western sides of the LMs decrease gradually from south to north, following a southwestward bias along the latitude. The high values of the TCCW and TP are predominantly concentrated in the period from July to September. The greatest difference between the two sides occurs in September, with the eastern side exhibiting 15% and 18% higher values compared to the western side, respectively. (2) Both the TCCW and TP exhibit distinct diurnal distribution patterns. The high values on the eastern side persist for a longer duration throughout the day compared to the western side, and they occur in more consecutive months. There is a certain correlation between the steepness of the eastern slope and the gentle gradient of the western slope. Additionally, the occurrence of these high values in the afternoon is earlier on the eastern side compared to the western side. (3) The monthly mean TP is significantly linearly correlated with the TCWV and TCCW, with slightly higher coefficients for the western side compared to the eastern side. This relationship is closely related to the topography of the mountain range. The regression equation provides a quantitative tool for predicting the monthly mean TP in the LM region and serves as a reference basis for the development of cloud water resources in the area.