Reanalysis Datasets Research Articles

Evaluating Apparent Temperature in the Contiguous United States From Four Reanalysis Products Using Artificial Neural Networks

AbstractReanalysis data sets are the products of data assimilation systems and while they exhibit consistency with the observed climate, they tend to be biased, especially at a local scale. In this study, we present an innovative approach for the assessment of the accuracy of four reanalysis data sets—European Center for Medium‐Range Weather Forecasts, Reanalysis 5 (ERA5), Modern‐Era Retrospective analysis for Research and Applications Version 2, North American Regional Reanalysis, and the Twentieth Century Reanalysis—in capturing daily and extreme observed apparent temperatures in over 300 stations in the contiguous United States (USA). We modeled the relationship between the reanalysis and observed data during a training period and subsequently used these models to predict daily observations during the test period. This process enabled potential adjustment of the biases in the reanalysis data sets (i.e., the predictors). For predictive modeling, the performance of a Feed‐Forward Neural Network (FFNN) and a linear regression (LR) were assessed. Our results show the data assimilations were closer to observations in the eastern parts compared to the western parts of the USA. Considering the number of stations with the lowest error and highest hit rate (for extreme events), FFNN outperformed LR except for extreme warm events where LR performed better. Among the reanalysis data sets, ERA5 consistently exhibited the highest performance in predicting both daily and extreme values of apparent temperature, with a larger margin of relative accuracy for cold extremes. Thus, ERA5 is more adept at accurately representing apparent temperatures in the USA.

Investigation of the atmospheric boundary layer characteristics over a high altitude station in North East India using measurements and reanalysis datasets

Investigation of the atmospheric boundary layer characteristics over a high altitude station in North East India using measurements and reanalysis datasets

Freddy: breaking record for tropical cyclone precipitation?

Abstract Depending on the location on the Earth, the amount of precipitation associated with tropical cyclones (TCs) can reach 20% of the total yearly precipitation over land and up to 40% over some ocean regions. TC induced freshwater flooding has been suggested to be the largest threat to human lives due to TCs. Therefore, a reliable quantification of the precipitation amount associated with each past TC is important for a better definition of the TC fingerprint on the climate. The temporal and horizontal resolution of state-of-the-art observational datasets and atmospheric reanalysis gives the possibility to quantify precipitation associated with TCs globally following the observed TC tracks. In this work we compare the TC-related precipitation in various observational and reanalysis datasets. A particular focus is given to the record-breaking TC Freddy (Southern Indian Ocean, 2023). Here we show that the time-varying bias in TC associated precipitation, due to the positive trend in assimilated observations, makes it difficult to assess long-term trend investigation based on reanalysis. To this aim we need to build on state-of-the-art general circulation models, free to evolve under historical radiative forcing.

Investigating the impact of coupling HARMONIE-WINS50 (cy43) meteorology to LOTOS-EUROS (v2.2.002) on a simulation of NO2 concentrations over the Netherlands

Abstract. Meteorological fields calculated by numerical weather prediction (NWP) models drive offline chemical transport models (CTMs) to solve the transport, chemical reactions, and atmospheric interaction over the geographical domain of interest. HARMONIE (HIRLAM ALADIN Research on Mesoscale Operational NWP in Euromed) is a state-of-the-art non-hydrostatic NWP community model used at several European weather agencies to forecast weather at the local and/or regional scale. In this work, the HARMONIE WINS50 (cycle 43 cy43) reanalysis dataset at a resolution of 0.025° × 0.025° covering an area surrounding the North Sea for the years 2019–2021 was coupled offline to the LOTOS-EUROS (LOng-Term Ozone Simulation-EURopean Operational Smog model, v2.2.002) CTM. The impact of using either meteorological fields from HARMONIE or from ECMWF on LOTOS-EUROS simulations of NO2 has been evaluated against ground-level observations and TROPOMI tropospheric NO2 vertical columns. Furthermore, the difference between crucial meteorological input parameters such as the boundary layer height and the vertical diffusion coefficient between the hydrostatic ECMWF and non-hydrostatic HARMONIE data has been studied, and the vertical profiles of temperature, humidity, and wind are evaluated against meteorological observations at Cabauw in The Netherlands. The results of these first evaluations of the LOTOS-EUROS model performance in both configurations are used to investigate current uncertainties in air quality forecasting in relation to driving meteorological parameters and to assess the potential for improvements in forecasting pollution episodes at high resolutions based on the HARMONIE NWP model.

Influence of wildfire emissions to carbon dioxide (CO2) observed at the Mt. Cimone station (Italy, 2165 m asl): A multi-year investigation

This work aimed at investigating on a multi-year framework (2015–2021) the contributions of wildfire emissions to atmospheric CO2 observed at the ICOS atmospheric class-2 station Mt. Cimone (CMN, 2165 m a.s.l. - Italy). Based on the analysis of a case study occurred in March 2020, a methodology providing indications about the possible presence of wildfire plumes from different European source regions was implemented. The methodology used observed CO at CMN, active fire detection by MODIS and air mass back-trajectories. An alternative detection method based on the use of a reanalysis dataset (CAMS) providing information about the amount of PM10 tagged to wildfires was also used.The results suggested that CMN could be affected by wildfire plumes for a fraction of time ranging from 1% to 10% (as a function of the adopted methodology setting). Over the studied period, we found a potentially important contribution of plumes from eastern Europe during October–April, while during May–September there was a prevalence from the Mediterranean sectors.Looking at the possible impact of these events to the observed CO2, we detected a notable increase of CO2 residuals with respect to periods not affected by fire perturbations during October–April (from +1.8 to +3.9 ppm, on average). We did not find evident impacts during the summer months, possibly due to a contribution by biospheric uptake during air mass transport to CMN (at least for a fraction of selected events).We discussed the sensitivity of results as a function of the selection methodology settings, suggesting that the strictest set-up based on the detection of large CO excesses could trace “major” events. A medium level of agreement was found when comparing the results of our selection methodology with CAMS reanalysis (fraction of PM10 emitted by wildfires).Even if it is still preliminary, our study indicated that the observations from CMN can represent, if supported by adequate diagnostic tools, a powerful dataset to evaluate the impact of wildfires to the atmospheric CO2 variability.

Revisiting two indices measuring high‐frequency daily variability

AbstractHigh‐frequency daily variability (HFDV), as a fundamental aspect of climate variability, has drawn increasing attention due to its crucial impacts on public health, regional ecosystems and economic growth rates. The statistical metrics are of great importance to measure the changes in HFDV. In this study, we compared two common statistical metrics of monthly HFDV, including the standard deviation (SD) and the day‐to‐day difference (DTDx), for various variables (temperature, specific humidity and horizontal wind) using the NCEP/NCAR reanalysis dataset. Here, we show that the SD of daily variables includes mainly sub‐semiannual variability (<180 days), while DTDx can separate the synoptic variability (<12 days, DTD1), submonthly variability (<25 days, DTD3) and subseasonal variability (<60 days, DTD5) from other temporal scales. Moreover, a wider dominant timescale of DTDx causes a higher climatological mean and a closer relationship with SD. We also find that the linear trend in SD exhibits remarkable differences from that of the DTDx nearly all over the world, which can be explained by the significant change in the autocorrelation with different lag days under climate change. Our findings highlight that the DTDx is superior to SD in measuring HFDV, especially in a changing climate.

Reconstructing long-term natural flows by ensemble machine learning

Large-scale human activities have caused significant disruption to the natural flow regime of rivers worldwide, leading to substantial deviations from river flows’ natural state. The natural hydrological regime of rivers holds great significance for the conservation and management of riverine ecosystems, which relies on the accurate reconstruction of natural river flows. However, the application of existing high spatiotemporal resolution large-scale natural flow datasets at the regional scale has not been adequately assessed, thereby limiting their regional-scale utility. In this study, utilizing global reanalysis datasets and observation data, in combination with ensemble machine learning models, we constructed long-term natural flow time series for inflows into the Pearl River Delta and evaluated the performance of these reconstructed inflows based on observed flow records during undisturbed periods. The reconstruction shows that the ensemble model, the AutoGluon model, indicates the best performance when compared with other machine learning models and outperforms the existing publicly available large-scale datasets in terms of evaluation metrics for the corresponding natural flows. The results of Indicator of Hydrologic Alteration and Range of Variability Analysis (IHA-RVA) indicate that climate variability, reservoir regulation and human activities have significantly altered the natural flow regime of the Pearl River Delta. Specifically, human activities generally tend to smooth natural flow variability in the Pearl River Delta. This research is expected to provide an efficient and reliable method for reconstructing natural flows and offers guidance for water resource management and ecosystem conservation.

Recent and projected changes in climate patterns in the Middle East and North Africa (MENA) region

Observational and reanalysis datasets reveal a northward shift of the convective regions over northern Africa in summer and an eastward shift in winter in the last four decades, with the changes in the location and intensity of the thermal lows and subtropical highs also modulating the dust loading and cloud cover over the Middle East and North Africa region. A multi-model ensemble from ten models of the Coupled Model Intercomparison Project—sixth phase gives skillful simulations when compared to in-situ measurements and generally captures the trends in the ERA-5 data over the historical period. For the most extreme climate change scenario and towards the end of the twenty-first century, the subtropical highs are projected to migrate poleward by 1.5°, consistent with the projected expansion of the Hadley Cells, with a weakening of the tropical easterly jet in the summer by up to a third and a strengthening of the subtropical jet in winter typically by 10% except over the eastern Mediterranean where the storm track is projected to shift polewards. The length of the seasons is projected to remain about the same, suggesting the warming is likely to be felt uniformly throughout the year.

Prediction of hourly wind speed time series at unsampled locations using machine learning

Various models for wind speed mapping have been developed, with increasing attention on models focusing on mapping wind speed distribution. This study extends these models to predict hourly wind speed time series at unsampled locations. A model based on the quantile mapping (QM) procedure was compared to a traditional and machine-learning model to interpolate wind speed spatially. These proposed models were also used with inputs from the ERA5 reanalysis dataset, enabling them to consider local variation in orography and large-scale wind fields. A widely used procedure for mean bias correction of reanalysis based on the Global Wind Atlas (GWA) was implemented and compared to the proposed models. It was found that the QM and machine learning model, both using input from ERA5, significantly outperformed GWA bias correction in terms of time series correlation and probability distribution. Despite being more computationally intensive than GWA bias correction, both models are recommended due to their significantly (in a statistical sense) superior performance.

Multi-scale analysis of satellite, reanalysis and muti-source precipitation estimates over the Tibetan Plateau

Due to the important hydrological and meteorological significance, the performance of different kinds of precipitation products for the Tibetan Plateau (TP) has been evaluated in previous studies. However, the performance of the products on a high spatial-temporal scale has not been clearly shown. Based on the Chinese Meteorological Administration (CMA) ground rain gauge observations during 2017–2018 over the TP, this paper analyzes the multi-scale performance of four satellite-based datasets (IMERG_Uncal, IMERG_Cal, GSMaP_MVK, and GSMaP_Gauge), two reanalysis datasets (ERA5 and ERA5-L), and one multi-source merged dataset (CMFD) for different precipitation intensities with a special attention to the characterization of spatiotemporal variability of heavy precipitation. Firstly, this study examines how the quality of precipitation products varies with time scale and precipitation intensity. CMFD generally shows the best quality and has the most stable quality at all temporal scales. Satellite products perform better in summer while reanalysis products show better results in cold seasons. Another significant issue is the impact of gauge-based calibration. GSMaP_Gauge with daily-scale gauge calibration is often considered to outperform other products. However, our study finds that GSMaP_Gauge is problematic at smaller spatiotemporal scales, especially in capturing heavy precipitation. The long-term performance of multi spatiotemporal scale comparison of different precipitation products is the focus of this study, which can help with their application and improvement.

Local and Object-Based Perspectives on Atmospheric Rivers Making Landfall on the Western North American Coastline

Abstract We apply the Ralph et al. scaling method to a reanalysis dataset to examine the climatology and variability of landfalling atmospheric rivers (ARs) along the western North American coastline during 1980–2019. The local perspective ranks AR intensity on a scale from 1 (weak) to 5 (strong) at each grid point along the coastline. The object-based perspective analyzes the characteristics of spatially independent and temporally coherent AR objects making landfall. The local perspective shows that the annual AR frequency of weak and strong ARs along the coast is highest in Oregon and Washington and lowest in Southern California. Strong ARs occur less frequently than weak ARs and have a more pronounced seasonal cycle. If those ARs with integrated water vapor transport (IVT) weaker than 250 kg m−1 s−1 are included, there is an enhanced seasonal cycle of AR frequency in Southern California and a seasonal cycle of AR intensity but not AR frequency in Alaska. The object-based analysis additionally indicates that strong ARs at lower latitudes are associated with stronger wind than weak ARs but similar moisture, whereas strong ARs at higher latitudes are associated with greater moisture than weak ARs but similar wind. For strong ARs, IVT at the core is largest for ARs in Oregon and Washington and smaller poleward and equatorward. Both IVT in the AR core and cumulative IVT along the coastline usually decrease after the first day of landfall for weak ARs but increase from the first to second day for strong ARs.

A satellite view of the exceptionally warm summer of 2022 over Europe

Abstract. Summer heatwaves are becoming increasingly dangerous over Europe, and their close monitoring is essential for human activities. Typically, they are monitored using the 2 m temperature from meteorological weather stations or reanalysis datasets. In this study, the 2022 extremely warm summer over Europe is analysed using satellite land surface temperature (LST), specifically the LSA SAF (Land Surface Analysis Satellite Application Facility) all-sky LST product (available from 2004 onwards). Since climate applications of LST are still poorly explored, heatwave diagnostics derived from satellite observations are compared with those derived using ERA5/ERA5-Land reanalysis data. Results highlight the exceptionality of 2022 in different metrics such as the mean LST anomaly, area under extreme heat conditions, number of hot days and heatwave magnitude index. In all metrics, 2022 ranked first when compared with the remaining years. Compared to 2018 (next in all rankings), 2022 exceeded its LST anomaly by 0.7 °C and each pixel had on average 7 more hot days. Satellite LST complements reanalysis diagnostics, as higher LST anomalies occur over areas under severe drought, indicating a higher control and amplification of the heatwave by surface processes and vegetation stress. These cross-cutting diagnostics increase the confidence across satellite data records and reanalyses, fostering their usage in climate applications.

Role of the Indian Ocean basin mode in driving the interdecadal variations of summer precipitation over the East Asian monsoon boundary zone

Abstract. Based on long-term observational and reanalysis datasets from 1901 through 2014, this study investigates the characteristics and physical causes of the interdecadal variations in the summer precipitation over the East Asian monsoon boundary zone (EAMBZ), which is a peculiar domain defined from the perspective of the interplay between climatic systems (i.e., mid-latitude westerly and East Asian summer monsoon). Observational evidence reveals that, similarly to previous studies, the EAMBZ precipitation featured prominent interdecadal fluctuations, e.g., with dry summers during the periods preceding 1927, 1939–1945, 1968–1982, and 1998–2010 and wet summers during the periods of 1928–1938, 1946–1967, and 2011 onwards. Further analyses identify that, amongst the major interdecadal oceanic forcings (e.g., Atlantic multidecadal oscillation and Pacific decadal oscillation), the Indian Ocean basin mode (IOBM) is a significant oceanic forcing responsible for the interdecadal variations of the EAMBZ precipitation, playing an independent and critical modulation role. When the cold phase of the IOBM occurs, an anomalous cyclonic circulation is excited around the northeast corner of the tropical Indian Ocean, which further induces a north-low–south-high meridional seesaw pattern over the Northeast China–subtropical western Pacific (SWP) sector. Such seesaw pattern is conducive to the enhanced EAMBZ precipitation by linking favorable environments for the transportation of water vapor from the SWP and the convergence over the EAMBZ at interdecadal timescales. For this reason, a physical–empirical model for the EAMBZ precipitation is developed in terms of the IOBM cooling. Despite the fact that the extreme summer EAMBZ precipitation cannot be captured by this model, it can still well capture its interdecadal fluctuations and reflect their steady relationship. The key physical pathway connecting the IOBM cooling with the interdecadal variations of the summer EAMBZ precipitation is supported by the numerical results based on the large ensemble experiment and the Indian Ocean pacemaker experiment. Our findings may provide new insights into the understanding of the causes of the interdecadal variations in the summer EAMBZ precipitation, which may favor the long-term policy decision-making for the local hydrometeorological planning.

Early Season Forecasting of Corn Yield at Field Level from Multi-Source Satellite Time Series Data

Crop yield forecasting during an ongoing season is crucial to ensure food security and commodity markets. For this reason, here, a scalable approach to forecast corn yields at the field-level using machine learning and satellite imagery from Sentinel-2 and Landsat missions is proposed. The model, evaluated on 1319 corn fields in the U.S. Corn Belt from 2017 to 2022, integrates biophysical parameters from Sentinel-2, Land Surface Temperature (LST) from Landsat, and agroclimatic data from ERA5 reanalysis dataset. Resampling the time series over thermal time significantly enhances predictive performance. The addition of LST to our model further improves in-season yield forecasting, through its capacity to detect early drought, which is not immediately visible to optical sensors such as the Sentinel-2. Finally, we propose a new two-stage machine learning strategy to mitigate early season partially available data. It consists in extending the current time series on the basis of complete historical data and adapting the model inference according to the crop progress.

Latent Heat Flux Trend and Its Seasonal Dependence over the East China Sea Kuroshio Region

Investigating latent heat flux (LHF) variations in the western boundary current region is crucial for understanding air–sea interactions. In this study, we examine the LHF trend in the East China Sea Kuroshio Region (ECSKR) from 1959 to 2021 using atmospheric and oceanic reanalysis datasets and find that the LHF has a significant strengthening trend. This strengthening can be attributed to sea surface warming resulting from the advection of sea surface temperatures. More importantly, the LHF trend has an apparent seasonal dependence: the most substantial increasing trend in LHF is observed in spring, while the trends are weak in other seasons. Further analysis illustrates that the anomaly of air–sea humidity difference plays a pivotal role in controlling the seasonal variations in LHF trends. Specifically, as a result of the different responses of the East Asian Trough to global warming across different seasons, during spring, the East Asian Trough significantly deepens, resulting in northerly winds that facilitate the intrusion of dry and cold air into the ECSKR region. This intensifies the humidity difference between the sea and air, promoting the release of oceanic latent heat. These findings can contribute to a better understanding of the surface heat budget balance within western boundary currents.

ASSESSMENT OF SOLAR PV OUTPUT PERFORMANCE WITH VARYING TILT ANGLES AND WEATHER DATA FROM ERA5: CASE OF MUNTINLUPA CITY, PHILIPPINES

Abstract. Solar photovoltaic (PV) technology has been gaining popularity in the Philippines as an alternative source of sustainable energy. In such technology, module tilt angle and weather conditions are among the system parameters that have substantial impacts on PV system performance. Previous studies have considered either tilt angles or weather conditions, but not the combined impact of these two parameters on solar PV power output. The objective of this study is to examine the effects of weather variables and tilt angle on the output of solar photovoltaic (PV) systems in Muntinlupa City, the Philippines. Three 120W monocrystalline solar PV panels were used and set up to three different tilt angles (i.e., 5°, 10°, and 15°). The fifth generation of the ECMWF's global climate and weather reanalysis (ERA5) dataset was used to gather hourly weather information such as surface solar radiation, wind speed, wind direction, relative humidity, ambient temperature, and total precipitation. Three principal components (PC), which together account for 95% of the variability, were identified using principal components analysis (PCA), which was used to address multicollinearity among the weather parameters. To assess the effects of tilt angle, time, and PCs on solar PV production, analysis of covariance (ANCOVA) was carried out. Results show that all weather variables, except for wind speed and total precipitation, have a significant impact on solar PV production with configuration producing the best results. Moreover, a significant difference in mean solar PV production was observed among the three tilt angles. From 6:00 AM to 2:00 PM, solar PV output gradually increases and declines thereafter. Outputs this study can help in optimizing the design and configuration of solar PV systems in the Philippines by considering weather variables and module tilt angle. Lastly, this study provides useful information for system designers, installers, and policymakers in improving energy generation and utilization, encouraging the use of renewable energy sources, and advancing sustainable energy objectives.

Rapid Acceleration of Arctic Near‐Surface Wind Speed in a Warming Climate

AbstractArctic near‐surface wind speed (NWS) plays an increasingly crucial role in influencing the local air‐sea interactions and the safety of trans‐Arctic shipping, but its potential changes in a warming climate and underlying causes remain unclear. Using reanalysis and model simulation data sets, we reveal that the Arctic NWS has increased remarkably since the 1960s, with the strongest increase in the Arctic Ocean surface. We propose that the acceleration of Arctic NWS is primarily driven by reduced stability in the lower troposphere due to increased upward heat fluxes and decreased surface roughness owing to the losses of Arctic glaciers and sea ice in a warming climate. In addition, the coupled climate models project a robust increase in the Arctic NWS under various warming scenarios during the 21st century, especially in the vicinity of the Kara Sea and the Beaufort Sea.

A conceptual model for evaluating the stability of high-altitude ice-rich slopes through coupled thermo-hydro-mechanical simulation

The collapse of glacial, permafrost, and ice-rich moraine slopes in high-altitude mountainous areas not only threatens downstream residents and infrastructure but also displaces ice mass to lower and warmer elevations, accelerating glacial ablation to some extent. Despite being considered rare, ice-rich slopes collapse more frequently than commonly thought due to climate change. For example, two adjacent mountain glaciers in the Aru Range of the Tibetan Plateau, characterized by large volumes (68 × 106 m3 and 83 × 106 m3, respectively) and low surface slope angles (12.3° and 12.9°, respectively), collapsed surprisingly in 2016. While the mechanisms behind these collapses have garnered broad attention, the ability to quantitatively assess the instability of ice-rich slopes remains limited due to the complex interplay of multi-physical processes. Taking the Aru glacier collapses as reference cases, this paper presents a conceptual model, implemented through coupled thermo-hydro-mechanical simulation, to evaluate the stability of high-altitude ice-rich slopes due to climate change, rainfall and ice ablation. Results indicate that the methodology captures the effects of temperature change, rainfall and meltwater on the instability events well, demonstrating promising potential in evaluating potential collapse zones of ice-rich slopes similar to the Aru glaciers. Furthermore, the role of climate change in the well-known Aru events is demonstrated using a state-of-the-art global climate reanalysis dataset. Findings reveal that the increase in liquid water infiltrating the Aru glaciers since 2010 was a critical factor leading to the instability events.

A multivariate probabilistic framework for tracking the regional tropical edges: analysis of inter-annual variations and long-term trends

In the present study, a multivariate probabilistic framework is used to identify the meridional positions of regional tropical edges (RTEs), which are based on two variables: sea level pressure and precipitation minus evaporation. This new defined metric effectively captures inter-annual variability and long-term trend of the commonly adopted zonal mean tropical edge based on meridional mass stream function and near-surface winds. Besides, pronounced RTE trends are primarily located over the oceanic regions, and the terrestrial areas exhibit substantial inter-annual variability. These results are consistent among three modern reanalysis datasets. Moreover, the impacts of climate modes on RTE are investigated. The El Niño-Southern Oscillation, the Atlantic multi-decadal oscillation, and the Southern Annular Mode are important both on the inter-annual variations and long-term trends of RTE. The Pacific Decadal Oscillation is more inclined to affect long-term contribution rather than inter-annual relationship, and the Pacific–North American teleconnection, the North Atlantic Oscillation, and the Arctic oscillation highlight the inter-annual relationship with RTE in the specific regions, such as North Pacific, North Atlantic, and North Africa, respectively.

Warm conveyor belt characteristics and impacts along the life cycle of extratropical cyclones: case studies and climatological analysis based on ERA5

Abstract. This study presents a systematic investigation of the characteristics and meteorological impacts of warm conveyor belts (WCBs). For this purpose, we compile a new WCB climatology (1980–2022) of trajectories calculated with the most recent reanalysis dataset ERA5 from the European Centre for Medium-Range Weather Forecasts (ECMWF). Based on this new climatology, two-dimensional masks are defined that represent the inflow, ascent, and outflow locations of WCBs. These masks are then used to objectively quantify the key characteristics (intensity, ascent rate, and ascent curvature) and meteorological impacts (precipitation and potential vorticity (PV) anomalies) of WCBs in order to (i) attribute them to different stages in the life cycle of the associated cyclones and to (ii) evaluate differences in the outflow of the cyclonic and anticyclonic branches. The approach was applied globally, but this study focuses on the North Atlantic, one of the regions where WCBs ascend most frequently. The method is first tested and illustrated through three case studies of well-documented cyclones, revealing both the similarities and the case-to-case variability in the evolution of the WCB characteristics and impacts. We then extend the analysis to about 5000 cyclones that occurred in winter between 1980–2022 in the North Atlantic. The case studies and the climatological analysis both show that WCBs are typically most intense (in terms of air mass transported, ascent rate, precipitation rate, and volume) during the intensification period of the associated cyclone. The northward displacement along the storm track and diabatic PV production lead to an increase in low-level PV in the region of WCB ascent during the cyclone life cycle. The negative PV anomaly at upper levels, associated with the WCB outflow, remains relatively constant. The investigation of the WCB branches reveals an increasing intensity of the cyclonic WCB branch with time, linked to the increasing strength of the cyclonic wind field around the cyclone. Due to a lower altitude, the outflow of the cyclonic WCB branch is associated with a weaker negative PV anomaly than the anticyclonic one, which ascends to higher altitudes. In summary, this study highlights the distinct evolution of WCB characteristics and impacts during the cyclone life cycle and the marked differences between the cyclonic and anticyclonic branches.