European Centre For Medium-Range Weather Forecasts Research Articles

Causes of growing middle-to-upper tropospheric ozone over the northwest Pacific region

Abstract. Long-term ozone (O3) changes in the middle-to-upper troposphere are critical to climate radiative forcing and tropospheric O3 pollution. Yet, these changes remain poorly quantified through observations in East Asia. Concerns also persist regarding the data quality of the ozonesondes available at the World Ozone and Ultraviolet Radiation Data Centre (WOUDC) for this region. This study aims to address these gaps by analyzing O3 soundings at four sites along the northwestern Pacific coastal region over the past 3 decades and by assessing their consistency with an atmospheric chemistry–climate model simulation. Utilizing the European Centre for Medium-Range Weather Forecasts (ECMWF) Hamburg (ECHAM)/Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) nudged simulations, it is demonstrated that trends between model and ozonesonde measurements are overall consistent, thereby gaining confidence in the model's ability to simulate O3 trends and confirming the utility of potentially imperfect observational data. A notable increase in O3 mixing ratio around 0.29–0.82 ppb a−1 extending from the middle troposphere to the upper troposphere is observed in both observations and model simulations between 1990 and 2020, primarily during spring and summer. The timing of these O3 tongues is delayed when moving from south to north along the measurement sites, transitioning from late spring to summer. Investigation into the drivers of these trends using tagged model tracers reveals that O3 of stratospheric origin (O3S) dominates the absolute O3 mixing ratios over the middle-to-upper troposphere in the subtropics, contributing to the observed O3 increases by up to 96 % (40 %) during winter (summer), whereas O3 of tropospheric origin (O3T) governs the absolute value throughout the tropical troposphere and contributes generally much more than 60 % to the positive O3 changes, especially during summer and autumn. During winter and spring, a decrease in O3S is partly counterbalanced by an increase in O3T in the tropical troposphere. This study highlights that the enhanced downward transport of stratospheric O3 into the troposphere in the subtropics and a surge of tropospheric O3 in the tropics are the two key factors driving the enhancement of O3 in the middle-to-upper troposphere along the northwest Pacific region.

Predictability of tropical cyclone track density in S2S reforecast

In this study, we examine the predictability of tropical cyclone (TC) track density in the subseasonal-to-seasonal (S2S) reforecast ensembles of the European Centre for Medium-range Weather Forecasts (ECMWF) using the method of average predictability time (APT). Eleven of the retrieved APT modes (APTMs) of TC track density possess an APT longer than 1 week. The most predictable of them, APTM-1, has an APT of almost three weeks and is found to be closely linked to the Boreal Summer Intraseasonal Oscillation (BSISO) and monsoon variability. Another discovery is the strong relationship between APTM-7 and the activity of mixed Rossby-gravity (MRG) waves and tropical depression (TD) type disturbances despite its short APT of ~12 days. We further carry out a simple case analysis to see how the relatively high predictability of APTM-1 manifests in the S2S model. Our work provides a new possibility for improving medium-range TC forecast skill, and has revealed how underlying tropical variability can play a role in determining TC predictability.

The added value and potential of long-term radio occultation data for climatological wind field monitoring

Abstract. Global long-term stable 3D wind fields provide valuable information for climate-oriented analyses of the dynamics of the atmosphere. Their monitoring remains a challenging task given the shortcomings of available observations. One promising option for progress is the use of radio occultation (RO) satellite data, which enable deriving dynamics based on thermodynamic data. In this study we focus on three main goals, explored through the fifth version of the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5) and RO datasets, using monthly-mean January and July data over 2007–2020. Our focus is on a 2.5° × 2.5° spatial synoptic scale over the free troposphere to the mid-stratosphere (i.e. 800–10 hPa). First, by comparing ERA5-derived geostrophic and gradient wind speeds to the original ERA5 ones, we examine the regions of validity of the studied approximations at a given synoptic-scale resolution. Second, to assess the possible added value of the RO-derived climatic winds in terms of their long-term stability, we test their consistency with the corresponding ERA5-derived winds. Third, by comparing the RO climatic winds to the original ERA5 winds, we evaluate the potential benefit of RO as an additional dataset for wind analyses and climate monitoring. With this three-step analysis, we decompose the total wind speed bias into the contributions from the approximation and the systematic difference between the RO and ERA5 datasets. We find that the geostrophic approximation is a valid method to estimate winds in the free troposphere, while the gradient wind approximation works better in the lower stratosphere. Both approximations generally work well over the mentioned altitudes, within an accuracy of 2 m s−1 for the latitudes 5–82.5°. Exceptions are found in winter in the monsoonal area and above larger mountain ranges in the free troposphere, as well as above the northern polar regions in the mid-stratosphere. RO- and ERA5-derived geostrophic winds mostly showed good agreement (within 2 m s−1). However, temporal change in the systematic difference higher than 0.5 m s−1 per decade was found. This points to a possible impact of changes in the source of the assimilated data in ERA5. The overall high accuracy of the monthly-mean wind fields, backed by the long-term stability and fine vertical resolution of the underlying RO data, highlights the added value and potential benefit of RO-derived climatic winds for climate monitoring and analyses.

Super Typhoons Simulation: A Comparison of WRF and Empirical Parameterized Models for High Wind Speeds

As extreme forms of tropical cyclones (TCs), typhoons pose significant threats to both human society and the natural environment. To better understand and predict their behavior, scientists have relied on numerical simulations. Current typhoon modeling primarily falls into two categories: (1) complex simulations based on fluid dynamics and thermodynamics, and (2) empirical parameterized models. Most comparative studies on these models have focused on wind speed below 50 m/s, with fewer studies addressing high wind speed (above 50 m/s). In this study, we design and compare four different simulation approaches to model two super typhoons: Typhoon Surigae (2102) and Typhoon Nepartak (1601). These approaches include: (1) The Weather Research and Forecasting (WRF) model simulation driven by NCEP Final Operational Global Analysis data (FNL), (2) WRF simulation driven by the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data (ERA5), (3) the empirical parameterized Holland model, and (4) the empirical parameterized Jelesnianski model. The simulated wind fields were compared with the measured wind data from The Soil Moisture Active Passive (SMAP) platform, and the resulting wind fields were then used as inputs for the Simulating WAves Nearshore (SWAN) model to simulate typhoon-induced waves. Our findings are as follows: (1) for high wind speeds, the performance of the empirical models surpasses that of the WRF simulations; (2) using more accurate driving wind data improves the WRF model’s performance in simulating typhoon wind speeds, and WRF simulations excel in representing wind fields in the outer regions of the typhoon; (3) careful adjustment of the maximum wind speed radius parameter is essential for improving the accuracy of the empirical models.

Assessing the spatiotemporal precipitation trends from ERA5-Land over Indonesia region

Abstract The availability and accuracy of high-resolution spatial and temporal climate data are required for various climate-related analyses, especially in areas with varying topography and climate. The long-term ERA5-Land reanalysis data from the European Centre for Medium-Range Weather Forecast (ECMWF) combines climate model and observation data from around the world into a complete and consistent collection with a spatial resolution of 0.1°. This high-resolution climate data is considered to be a reliable gridded dataset spanning various regions. Due to the scarcity of ground-based data in Indonesia, ERA5-Land is expected as an alternative to represent the climate condition. However, a comprehensive assessment prior to usage should be conducted. This study evaluates the proficiency of ERA5-Land based on observation data from the Climate Hazard Group InfraRed Precipitation with Station data (CHIRPS) to represent the precipitation variability over Indonesia during 1990-2022. Comprehensive investigations on the spatial-temporal dynamics of precipitation are presented. The ERA5-Land precipitation trend shows a similar pattern in annual total accumulated precipitation and precipitation cycle to CHIRPS observations. In areas with monsoon patterns like south Sumatera, Java, southern Kalimantan, Sulawesi, and part of Papua, low bias is observed often during the June-July-August (JJA) dry season, while high bias is observed often during the December-January-February (DJF) and September-October-November (SON). The similar observational trends and acceptable bias of ERA5-Land precipitation data convince the use of the data in simulating precipitation variability in the Indonesian region, which is essential for further climate studies.

Analysis of Dust Storm Intensity over Baghdad City

Background: Semi-arid regions of the world experience diverse climates and weather events, including dust storms. These storms suspend and transport particles in the atmosphere. Objective: The Iraqi Meteorological Organization and Seismology (IOM) is a key data source for researchers studying these dynamics, offering visibility data for Baghdad from several years (2005, 2008, 2009, 2011, 2018, 2022). Baghdad’s data is also included in the ERA-5 dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF), which provides precise hourly air temperature data at 2 meters above ground level. Methods: The study investigates the correlation between air temperature and dust storm severity in semi-arid regions, particularly during storm waves, which can constitute up to half of a storm’s duration. The severity of dust storms, influenced by temperature variations, is crucial in understanding storm characteristics. Results: The findings underscore the significance of air temperature in understanding dust storms’ behavior, aiding in refining forecasting models and emergency planning in vulnerable areas. The detailed spatial and temporal data from the study provide a foundation for in-depth research into the complexities of dust storms. Conclusions: By analyzing visibility and temperature records, the study emphasizes the role of climatic factors in shaping dust storm behavior in semi-arid regions.

Impact of ensemble‐based hybrid background‐error covariances in ECMWF's next‐generation ocean reanalysis system

AbstractAn ensemble of data assimilations (EDA) can provide valuable information on analysis and short‐range forecast uncertainties. The present European Centre for Medium‐Range Weather Forecasts (ECMWF) operational ocean analysis and reanalysis system, called the Ocean Reanalysis System 5 (ORAS5), produces an ensemble but does not exploit it for the specification of the background‐error covariance matrix , a key component of the data assimilation system. In this article, we describe EDA developments for the ocean, which take advantage of the short‐range forecast ensemble for specifying, in two distinct ways, parameters of a covariance model representation of . First, we generate a climatological ensemble over an extended period to produce seasonally varying climatological estimates of background‐error variances and horizontal correlation length‐scales. Second, in each assimilation cycle, we diagnose flow‐dependent variances from the ensemble and blend them with the climatological estimates to form hybrid variances. We also use the ensemble to diagnose flow‐dependent vertical correlation length‐scales. We demonstrate for the Argo‐rich period that this new, ensemble‐based hybrid formulation of results in a significant reduction of background errors compared with the parameterized formulation of used in ORAS5, with the globally averaged reduction ranging between 5% and 15% for subsurface temperature and salinity and up to and for sea‐surface temperature and sea‐surface height, respectively. The new ocean EDA system will be employed in ORAS6, ECMWF's next‐generation ocean reanalysis system.

Assessment of Hydrologic Data Estimates From ERA5 Reanalyses in Benin, West Africa

ABSTRACTIn West Africa, the validation of distributed models is limited by the quality and availability of point station data measured in situ. ERA5 is a climate reanalysis product produced by the European Centre for Medium‐range Weather Forecasts (ECMWF) and is suggested to overcome this constraint. This study assessed and compared the quality of ERA5 and its variant ERA5‐Land (namely, LAND) over Benin at spatial and monthly time scales. ERA5 relies on a single‐level version with a 0.25° × 0.25° resolution, while LAND is a land surface version with a 0.1° × 0.1° resolution. Four variables were collected, namely, surface runoff (SRO), evapotranspiration (PET), water table depth (WTD) and soil water content (SWC). Single nearest pixel (SNP) and inverse distance weighting (IDW) selection methods were used to compare the reanalyse data to point station data based on the correlation (c), mean absolute error (MAE) and relative mean absolute error (RMAE). With the SNP method, both reanalyses showed a best peak simulation in mean SRO. Their performance in terms of correlation ranged from 0.26 to 0.65 for ERA5 vs. 0.34 to 0.60 for LAND. The reanalyses showed high correlations (generally > 0.80) for SWC and for the PET (sometime greater than 0.90). The correlations were below 0.5 in both reanalyses for the WTD, with slight overestimations (4.73 m for ERA5 vs. 3.13 m for LAND). Similar results were reported with the IDW selection method. One or the other of the two reanalyses can be recommended for model calibration/validation, but care must be taken in the choice because the one chosen may be better in terms of correlation even though it has significant biases and vice versa. Correcting the variables of these reanalysis datasets could also improve their performance.

Significant Wave Height Retrieval in Tropical Cyclone Conditions Using CYGNSS Data

The retrieval of global significant wave height (SWH) data is crucial for maritime navigation, aquaculture safety, and oceanographic research. Leveraging the high temporal resolution and spatial coverage of Cyclone Global Navigation Satellite System (CYGNSS) data, machine learning models have shown promise in SWH retrieval. However, existing models struggle with accuracy under high-SWH conditions and discard a significant number of such observations due to low quality, which limits their effectiveness in global SWH retrieval, particularly for monitoring tropical cyclone (TC) events. To address this, this study proposes a daily global SWH retrieval framework through the enhanced eXtreme Gradient Boosting model (XGBoost-SC), which incorporates Cumulative Distribution Function (CDF) matching to introduce prior distribution information and reduce errors for SWH values exceeding 3 m. An enhanced loss function is employed to improve accuracy and mitigate the distribution bias in low-SWH retrieval induced by CDF matching. The results were tested over one million sample points and validated against the European Centre for Medium-Range Weather Forecasts (ECMWF) SWH product. With the help of CDF matching, XGBoost-SC outperformed all models, significantly reducing RMSE and bias while improving the retrieval capability for high SWHs. For SWH values between 3–6 m, the RMSE and bias were 0.94 m and −0.44 m, and for values above 6 m, they were 2.79 m and −2.0 m. The enhanced performance of XGBoost-SC for large SWHs was further confirmed in TC conditions over the Western North Pacific and in the Western Atlantic Ocean. This study provides a reference for large-scale SWH retrieval, particularly under TC conditions.

Distinguishing north and south African Easterly Waves with a spectral method: Implication for tropical cyclogenesis from mergers in the North Atlantic

AbstractAfrican Easterly Waves (AEWs) are mixed barotropic–baroclinic instabilities, propagating both north (AEW‐N) and south (AEW‐S) of the African Easterly Jet. They are an important component of the North Atlantic region meteorology, influencing precipitation and convection, and are primary seeds for tropical cyclones (TCs) over the North Atlantic. In this study, a novel tracking protocol is introduced based on filtering of the 700‐hPa vorticity over two frequency ranges, suited for each wave track: 2.95–4.55 days (quicker propagating mode) and 4.55–6.35 days (slower propagating mode). It is applied to a 31‐year reanalysis dataset, the European Centre for Medium‐range Weather Forecasts (ECMWF) Fifth Reanalysis. This method effectively isolates north‐track activity even amidst dominant south‐track signals, enabling better identification and tracking of northward vortices. An analysis of the climatological structure of the two modes is conducted. The slower mode propagates around and transitions from 850 hPa above the continent to 700 hPa above the ocean, consistently with AEW‐N characteristics. The quicker mode propagates around and is located at the African Easterly Jet level, between 700 and 600 hPa above the continent and the ocean, consistently with AEW‐S characteristics. The new method allows the detection of AEW‐N even when AEW‐S activity dominates the 2–10 day signal, thereby enabling the detection of more AEW‐S/AEW‐N mergers than in previous studies. When comparing the usual 2–10 day filtering with this new method, the proportion of TCs related to AEW mergers rises from 11% to 25%. 23.4% of mergers are found to be related to TC genesis, making mergers the most efficient precursors for TC genesis in the Atlantic. TC genesis from mergers occurs more frequently on the eastern side of the basin than TC genesis from other precursors.

Predicting the next decade of sea surface temperatures in the Mediterranean Sea using hybrid deep learning models

The Mediterranean Sea plays a crucial role in regulating regional climate, supporting biodiversity, and sustaining coastal economies, making its temperature an essential factor for environmental stability. This study presents a forecast of sea level temperatures in the Mediterranean Sea for the next 10 years using historical data from the European Centre for Medium-Range Weather Forecasts (ECMWF), spanning from 1940 to 2024. Two hybrid deep learning models, CNN-LSTM and CNN-GRU, are employed to predict future temperature trends. The models are evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) as primary accuracy metrics. The results will provide valuable insights into the potential impacts of climate change on the Mediterranean region’s sea level temperatures, contributing to better understanding and future planning efforts. Keywords: Climate Change, Forecasting, Mediterranean, Deep Learning, Hybrid Deep Learning

Consideration of the cloud motion for aircraft-based stereographically derived cloud geometry and cloud top heights

Abstract. Cloud geometry and in particular cloud top heights can be derived from 2-D camera measurements by applying a stereographic method to data from an overflight over a scene of clouds (see, e.g., Kölling et al., 2019). Although airplane overpasses are relatively fast, cloud motion with the wind is important and can result in errors in the cloud localization. Here, the impact of the wind is investigated using the method from Kölling et al. (2019) for measurements of the airborne hyperspectral imaging system spectrometer of the Munich Aerosol Cloud Scanner (specMACS). Further, a method for cloud motion correction using model winds from the European Centre for Medium-Range Weather Forecasts (ECMWF) is presented. It is shown that the update is important as the original algorithm without the cloud motion correction can over- or underestimate the cloud top heights by several hundred meters, depending on the wind speed and the relative wind direction. This is validated using data from the Elucidating the role of clouds–circulation coupling in climate (EUREC4A) campaign and realistic 3-D radiative transfer simulations. From the comparison of the derived cloud top heights with the expected ones from the model input, an average accuracy of the cloud top heights of less than (20±140) m (mean deviation and 1 standard deviation) is estimated for the updated method.

A revised ocean mixed layer model for better simulating the diurnal variation in ocean skin temperature

Abstract. Sea surface temperature (SST) is a crucial parameter in climate, weather, and ocean sciences due to its decisive role in ocean–atmosphere interactions. Identifying errors in the prognostic scheme used by the current European Centre for Medium-range Weather Forecasts (ECMWF) model for predicting the diurnal variation in ocean skin temperature led to a revisit and revision of the ocean mixed layer (OML) model. Validation of the revised model was conducted by comparing simulated temperatures at the sub-skin level and 1 m depth with observations from shipborne infrared measurements and buoys. These comparisons revealed a strong correlation, with an absolute mean deviation of less than 0.1 K and a standard deviation under 0.5 K, which are found to be comparable to errors in satellite observations of SST. Given that these results are derived from the same model simulations, the error statistics for the simulated skin temperature and its diurnal variation should have the same degree of accuracy. Furthermore, the simulation results closely align with anticipated solar radiation distributions, whereas ERA5 ocean skin temperature shows a significant lack of alignment with solar radiation. Consequently, the revised OML model shows promising potential for improving the simulation of diurnal SST variations in weather and climate models.

The impact of quasi-biennial oscillation (QBO) disruptions on diurnal tides over the low- and mid-latitude mesosphere and lower thermosphere (MLT) region observed by a meteor radar chain

Abstract. A quasi-biennial oscillation (QBO) disruption is a very rare phenomenon in which QBO westward wind is temporarily interrupted by the occurrence of a band of westward wind in the tropical stratosphere. This phenomenon is important as it could greatly affect the global atmospheric circulation, especially in the mesosphere. Past observational and modelling studies have shown the QBO varying mesospheric diurnal tide, but the mechanism is still not fully understood. In this study, we report on the strong response of mesospheric diurnal tides to the two QBO disruptions that occurred in 2015–2016 and 2019–2020 and their possible mechanisms. The diurnal tidal winds are observed by a meteor radar chain, consisting of meteor radars located at Kunming (25.6° N, 103.8° E), Wuhan (30.5° N, 114.2° E), Mengcheng (33.4° N, 116.5° E), Beijing (40.3° N, 116.2° E), and Mohe (53.5° N, 122.3° E) in China. These observations provide clear evidence that mesospheric diurnal tides are unusually weakened (by ∼ −6 m s−1) during these QBO disruptions, over Kunming, Wuhan, Mengcheng, and Beijing. Using the Specific Dynamics version of the Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension (SD-WACCM-X) and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) dataset, the analysis indicates that the QBO wind affects mid-latitude mesospheric diurnal tides by modulating both the solar radiative absorption by subtropical stratospheric ozone (∼ 5 to 0.5 hPa) and the tidal–gravity wave interaction in the mesosphere (∼ 60 to 100 km). Thus, these unexpected QBO disruptions provide an opportunity to better understand the coupling between climate change and middle-atmospheric dynamics.

Perspective of explosive cyclones with three shapes of upper-level potential vorticity over the northern Atlantic Ocean

The characteristics of explosive cyclones (ECs) with three shapes of upper-level potential vorticity (PV), namely “streamer”, “hook”, and “treble clef” shapes were jointly examined by composite and diagnostic approaches, by using the fifth-generation global atmospheric reanalysis data (ERA5) issued by European Centre for Medium-Range Weather Forecasts (ECMWF). Totally, 48 ECs over the northern Atlantic Ocean with typical evolution from “streamer” to “hook”, and then to “treble clef” shape of PV were selected for composite analyses. It turns out that the upper-level PV exhibits “streamer”, “hook” and “treble clef” features at early, rapid deepening and mature stages of EC, respectively. The intrinsic essences of three shapes of upper-level PV associated with different stages of EC were linked. In the upper troposphere, positive PV tendency is mainly derived from positive horizontal advection of PV (HADV), which is essentially generated by high-PV air trapped in upper-level jet and cyclonic circulation. Positive HADV is the main factor driving the evolution of upper-level PV from “streamer” to “hook”, and then to “treble clef”. In the middle troposphere, positive PV tendency is primarily associated with positive vertical advection of PV (VADV). This is due to the upper-level high-PV anomalies from stratospheric intrusion and the lower-level high-PV anomalies generated by diabatic heating both extend vertically to the middle troposphere. In the lower troposphere, diabatic heating (DIAB) overwhelmingly produce positive PV tendency. These three PV anomalies are usually vertically overlain and connected to form PV “tower” that runs through the whole troposphere. The formation of PV “tower” may strengthen cyclonic circulation, which is in turn conducive to the explosive deepening of cyclone.

Global Distributions of Atmospheric Turbulence Estimated Using Operational High Vertical-Resolution Radiosonde Data

Abstract Atmospheric turbulence plays a key role in the mixing of trace gases and diffusion of heat and momentum, as well as in aircraft operations. Although numerous observational turbulence studies have been conducted using campaign experiments and operational data, understanding the turbulence characteristics particularly in the free atmosphere remains challenging due to its small-scale, intermittent, and sporadic nature, along with limited observational data. To address this, turbulence in the free atmosphere is estimated herein based on the Thorpe method by using operational high vertical-resolution radiosonde data (HVRRD) with vertical resolutions of about 5 or 10 m across near-global regions, provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) via the U.S. National Centers for Environmental Information (NCEI) for 6 years (October 2017–September 2023). Globally, turbulence is stronger in the troposphere than in the stratosphere, with maximum turbulence occurring about 6 km below the tropopause, followed by a sharp decrease above. Seasonal variations show strong tropospheric turbulence in summer and weak turbulence in winter for both hemispheres, while the stratosphere exhibits strong turbulence during spring. Regional analyses identify strong turbulence regions over the South Pacific and South Africa in the troposphere and over East Asia and South Africa in the stratosphere. Notably, turbulence information can be provided in regions and high altitudes that are not covered by commercial aircraft, suggesting its potential utility for both present and future high-altitude aircraft operations.

EVALUATION OF DAILY SATELLITE AND REANALYSIS OF RAINFALL DATA OVER SOUTH SUMATRA REGION

The limitations of good rainfall data due to constraints on direct measurements can be overcome by using satellite data or reanalysis data. The use of this data must, of course, go through a validation process first. This research aims to evaluate daily data from the Tropical Rainfall Measurement Mission (TRMM) version 3B42RT (TRMM_3B42RT) and European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) data against data collected from 17 rain gauges in the Sumatra region South. Evaluation is carried out based on the Correlation Coefficient (CC), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Bias Error (MBE) values between the data. In addition, the estimation capabilities of TRMM_3B42RT and ERA5 were evaluated based on the Probability of Detection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI) values. The results show a very high correlation between TRMM_3B42RT and ERA5 with rain gauge data, especially in terms of monthly data. These values (monthly data) for TRMM_3B42RT and ERA5 data are 0.3-0.9 and 0.2-0.9, respectively. The RMSE values of TRMM_3B42RT and ERA5 data in monthly analysis are 75-250mm/month and 100-180mm/month, respectively. The forecasting performance of TRMM_3B42RT and ERA5 shows good results, especially for moderate rainfall in daily data and heavy rainfall in monthly data. The results of this analysis show tha the monthly data TRMM_3B42RT is more in line with the station data and can be used in further research.

Statistical calibration of probabilistic medium-range Fire Weather Index forecasts in Europe

Abstract. Wildfires are increasing in frequency and severity across Europe, which makes accurate wildfire risk estimation crucial for decision-makers and emergency responders. Wildfire risk is usually estimated using meteorological-based fire weather indices such as the Canadian Forest Fire Weather Index (FWI). By using weather forecasts, the FWI can be predicted for several days and even weeks ahead. Probabilistic ensemble forecasts require verification and calibration in order to provide reliable and accurate forecasts, which are crucial for informed decision-making and an effective emergency response. In this study, we investigate the potential of non-homogeneous Gaussian regression (NGR) for statistically calibrating ensemble forecasts of the FWI. The FWI is calculated using medium-range ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) with lead times up to 15 d over Europe. The method is tested using a 30 d rolling training period and dividing the European region into three training areas (northern, central, and Mediterranean Europe). The calibration improves FWI forecast particularly at shorter lead times up to 84 h and in regions with elevated FWI values, i.e. areas with a higher wildfire risk such as central and Mediterranean Europe. The study demonstrates that NGR can be used to improve probabilistic FWI forecasts especially in the time range most critical for firefighting resource management and thereby supporting effective wildfire response strategies.

Characterizing Long-term Astroclimate Parameters at the Muztagh-Ata Site in the Pamir Plateau with ERA5 and MERRA-2 Data

Abstract The Muztagh-Ata site, an excellent high-altitude ground-based astronomical observing site was discovered and monitored in the eastern part of the Pamir Plateau in the southwestern Xinjiang Uygur Autonomous Region of China. This site has been systematically monitored using various observational parameters since the spring of 2017. Yet, the site lacks long-term monitoring and statistical characterization of key variables such as: precipitable water vapour (PWV) and air temperature. These factors directly influence whether a site is suitable for hosting large ground-based telescopes across optical, infrared, submillimeter, and millimeter wavelengths in later stages. In this study, we utilized atmospheric reanalysis datasets from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and ERA5, the fifth edition of the European Centre for Medium-Range Weather Forecasts (ECMWF). These datasets were combined with local observations from the weather station at the Muztagh-Ata site. Following the validation of ground-based and satellite data, we conducted a comparative analysis of PWV and temperature trends at the Muztagh-Ata site over a period of 24 years. The weighted annual mean nighttime temperature and PWV increase at rates of 0.18 ∼ 0.38 ○C decade−1 and 0.02 ∼ 0.15 mm decade−1, respectively. The nighttime PWV slightly decrease during the winter with rates of −0.01 ∼ −0.03 mm decade−1. This findings reveal that the PWV and temperature variation patterns at the Muztagh-Ata site are consistently stable, particularly in the results derived from the ERA5 dataset. The comprehensive conditions at this site are highly suitable and advantageous for hosting large optical, infrared, submillimeter, and millimeter wavelengths telescope installations.

Methods and Evaluation of AI-Based Meteorological Models for Zenith Tropospheric Delay Prediction

Zenith Tropospheric Delay (ZTD) is a significant error source affecting the accuracy of certain space geodetic measurements. This study evaluates the performance of Artificial Intelligence (AI) based meteorological models, such as Fengwu and Pangu, in estimating real-time ZTD. The results from these AI models were compared with those obtained from the Global Navigation Satellite System (GNSS), the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis (ERA5), and the third generation of the Global Pressure–Temperature data model (GPT3) to assess their accuracy across different time intervals, seasons, and geographic locations. The findings reveal that AI-driven models, particularly Fengwu, offer higher long-term forecasting accuracy. An analysis of data from 81 stations throughout 2023 indicates that Fengwu’s 7-day ZTD forecast achieved an RMSE of 2.85 cm when compared to GNSS-derived ZTD. However, in oceanic regions and areas with complex climatic dynamics, the Fengwu model exhibited a larger error compared to in other land regions. Additionally, seasonal variations and station altitude were found to influence the accuracy of ZTD predictions, emphasizing the need for detailed modeling in complex climatic zones.