Centre forMedium-Range Weather Forecasts Research Articles

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

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

The influence of plant water stress on vegetation–atmosphere exchanges: implications for ozone modelling

Abstract. Evapotranspiration is important for Earth's water and energy cycles as it strongly affects air temperature, cloud cover, and precipitation. Leaf stomata are the conduit of transpiration, and their opening is sensitive to weather and climate conditions. This feedback can exacerbate heat waves and can play a role in their spatiotemporal propagation. Sustained high temperatures strongly favour high ozone levels, with significant negative impacts on air quality and thus on human health. Our study evaluates the process representation of evapotranspiration in the atmospheric chemistry–climate European Centre for Medium-Range Weather Forecasts – Hamburg(ECHAM)/Modular Earth Submodel System (MESSy) Atmospheric Chemistry model. Different water stress parameterizations are implemented in a stomatal model based on CO2 assimilation. The stress factors depend on either soil moisture or leaf water potential, which act on photosynthetic activity, and mesophyll and stomatal conductance. The new functionalities reduce the initial overestimation of evapotranspiration in the model globally by more than an order of magnitude, which is most important in the Southern Hemisphere. The intensity of simulated warm spells over continents is significantly improved. For ozone, we find that a realistic model representation of plant water stress suppresses uptake by vegetation and enhances photochemical production in the troposphere. These effects lead to an overall increase in simulated ground-level ozone, which is most pronounced in the Southern Hemisphere over the continents. More sophisticated land surface models with multi-layer soil schemes could address the uncertainties in representing plant dynamics representation due to too-shallow roots. In regions with low evaporative loss, the representation of precipitation remains the largest uncertainty.

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

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

Global-scale evaluation of precipitation datasets for hydrological modelling

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

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

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

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

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

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

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

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

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

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

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

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

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

The Development of a Hailstone Disdrometer and Its Preliminary Observation in Aksu, Xinjiang

Hailfall is a severe local weather event that can cause great economic losses as well as the loss of people’s property; however, it is still difficult for domestic meteorological stations to comprehensively observe hail, and domestic independently developed hail observation instruments are still scarce. To help enable better automatic hail observations, a new independently developed hailstone disdrometer based on the acoustic principle, which can be used to measure the hailstone number and particle size and to calculate the corresponding equivalent liquid precipitation of hailstones, is proposed in this paper. The characteristics of hailstones were preliminarily analyzed using observation data from two hailstone disdrometers installed in Aksu, Xinjiang, where three hail events were observed via the hailstone disdrometer in the summer of 2023. By analyzing the development of deep convection clouds using the Fengyun 4A satellite-based cloud-top brightness temperature, and synoptic conditions based on the fifth-generation global climate reanalysis dataset produced by the European Centre for Medium-Range Weather Forecasts (the ECMWF ERA5 dataset), the hail formation mechanism was investigated in detail for one hailfall event. Accurate hail observations are an important basis for understanding spatiotemporal hail variation. The hailstone disdrometer proposed in this study offers a useful approach for domestic hail observation to provide first-hand hail information for the inspection of weather modification effects and disaster prevention and reduction.

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

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

Evaluation of Subseasonal Forecast Skill for Northern Hemisphere Winter Snow Cover

Abstract Accurate subseasonal forecasts for snow cover have significant socioeconomic value. This paper evaluates subseasonal forecasts for winter snow cover in the Northern Hemisphere as predicted by three numerical models: the Model for Prediction Across Scales – Atmosphere (MPAS), the China Meteorological Administration (CMA) model, and the European Centre for Medium-Range Weather Forecasts (ECMWF) model. While these models can generally simulate the spatial distribution of winter snow cover climatology and subseasonal variability, they tend to underestimate both the climatology and the intensity of subseasonal variability. Compared to persistence forecasts, these models demonstrate skill in subseasonal snow cover forecasting. Notably, the ECMWF model outperforms the MPAS and CMA models. The sensitivity of the surface air temperature subseasonal forecast skill to the predicted snow cover was also investigated using the MPAS. The results show that for forecasts with lead times of 1 to 2 weeks, the predicted snow cover contributes to the temperature forecasting skill. However, for forecasts with lead times of 3 to 4 weeks, the predicted snow cover does not enhance the temperature forecasting skill. Furthermore, part of the errors in temperature forecasts can be attributed to inaccuracies in snow cover forecasts with lead times of 2 weeks or more. These findings suggest that refining snow cover parameterization schemes and effectively exploiting predictability from snow cover can enhance the skill of subseasonal atmospheric forecasts.

Improving dynamical-statistical subseasonal precipitation forecasts using deep learning: A case study in Southwest China

Subseasonal precipitation forecasting is challenging but critical for water management, energy supply, and disaster prevention. To improve regional subseasonal precipitation prediction, previous studies have proposed a dynamical-statistical projection model (DSPM). In this study, we develop a new method that combines the DSPM and deep learning (DL), called the DL-DSPM. The DSPM is developed using the observed relationships between large-scale atmospheric circulations and regional precipitation, and the dynamical forecasted atmospheric circulations from the European Centre for Medium-Range Weather Forecasts (ECMWF) model. The DL-DSPM improves upon the DSPM by correcting biases in atmospheric circulation forecasts from the ECMWF model using two DL models, namely, residual network and U-Net models. In the case of Southwest China (SWC), DL models can improve atmospheric circulation forecasts at lead times beyond 5 pentads, including large-scale drivers of SWC precipitation variability. The DL-DSPM outperforms the ECMWF model and DSPM forecasts in predicting precipitation anomalies beyond 4 and 5 pentads over most SWC regions, respectively. In addition, the DL-DSPM is more skillful than the ECMWF model and DSPM in predicting extreme precipitation events more than 4 pentads in advance. The successful combination of DL and the DSPM provides a new possible direction for DL applications in subseasonal precipitation forecasting.

Spatiotemporal variability of surface ozone and associated meteorological conditions over the Arabian Peninsula

This study investigates the spatiotemporal variability of surface ozone (O3) over the Arabian Peninsula (AP) between 2005 and 2019, focusing on the Arabian Gulf (AG). The analysis explores the relationship between surface O3 data from the Copernicus Atmosphere Monitoring Service (CAMS) with boundary layer height (BLH), 2 m temperature (T2M), downward ultraviolet radiation at the surface (UVB), and 10 m wind speed (WS) and direction from the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5). Also, the study considers Carbon Monoxide (CO) and Nitrogen Oxides (NOx) surface emissions from the Tropospheric Chemical Reanalysis version 2 (TCR-2). Furthermore, it investigates the impact of El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) on surface O3 variations on a seasonal scale. Surface O3 observations from 15 ground-based stations across the AP were used to evaluate CAMS-O3, showing a good agreement between CAMS and the observations. The analysis of mean diurnal variations of CAMS-O3 and ERA5 reveals that surface O3 is highest over the eastern parts of the AP, mainly the AG, peaking during summer, followed by spring, fall, and winter. This seasonal cycle is also observed, to a large degree, in BLH, T2M, UVB, and WS. The results also reveal insignificant correlation between surface O3 and ENSO, but stronger correlation with IOD, especially over the AG during summer and fall. The analysis indicates that elevated T2M and UVB during daytime and elevated BLH during nighttime are significant contributors to increased levels of O3 over the AG.

Wave retrieval for Sentinel-1 synthetic aperture radar under complex sea state

ABSTRACT In this paper, sea surface waves during complex sea state (i.e. tropical cyclones (TCs) and Kuroshio current) are inverted from dual-polarized (vertical – vertical (VV) and vertical – horizontal (VH)) synthetic aperture radar (SAR) images using an existing algorithm, namely, the parameterized first-guess spectrum method (PFSM). In particular, the wind term is included in two modulation transfer functions (MTFs), i.e. tilt modulation and velocity bunching. More than 3000 Sentinel (S-1) images acquired in interferometric wide (IW) mode and extra wide (EW) mode are collected, and the operational CyclObs wind product provided by the French Research Institute for Exploitation of the Oceans (IFREMER) is collected corresponding to the TC images. Note that these images are located within 500 km away from TC eyes. The wave spectra during the TCs are then inverted using the PFSM algorithm and the SAR-derived wind product. Simultaneously, the 0.05° gridded wave spectra collocated with these images are simulated using a third-generation numeric wave model, called WAVEWATCH-III (WW3), in which the reconstructed wind based on the European Centre for Medium-Range Weather Forecasts (ECMWF), the Copernicus Marine Environment Monitoring Service (CMEMS) current, and the CMEMS sea level are treated as the forcing field. Comparison of the SAR wave retrievals and simulations reveals that the root mean square errors (RMSEs) of the significant wave height (SWH) and mean wave period (MWP) are 0.55 m and 0.43 s, their correlation coefficients (COR) are 0.94 and 0.92, and their scatter index (SI) values are 0.17 and 0.07. These results demonstrate the better performance of the PFSM algorithm using theoretical MTFs, i.e. an RMSE of 0.68 m and COR of 0.90 for the SWH and an RMSE of 0.60 s and a COR of 0.87 for the MWP. Although the accuracy of the wave retrieval from the SAR image under complex sea state is improved when implementing the tilt and velocity bunching MTFs with the strong wind term, SWH retrievals by algorithm PFSM suffer saturation problem at SWH > 8 m.

Performance Evaluation of a National Seven-Day Ensemble Streamflow Forecast Service for Australia

The Australian Bureau of Meteorology offers a national operational 7-day ensemble streamflow forecast service covering regions of high environmental, economic, and social significance. This semi-automated service generates streamflow forecasts every morning and is seamlessly integrated into the Bureau’s Hydrologic Forecasting System (HyFS). Ensemble rainfall forecasts, European Centre for Medium-Range Weather Forecasts (ECMWF), and Poor Man’s Ensemble (PME), available in the Numerical Weather Prediction (NWP) suite, are used to generate these streamflow forecasts. The NWP rainfall undergoes pre-processing using the Catchment Hydrologic Pre-Processer (CHyPP) before being fed into the GR4H rainfall–runoff model, which is embedded in the Short-term Water Information Forecasting Tools (SWIFT) hydrological modelling package. The simulated streamflow is then post-processed using Error Representation and Reduction In Stages (ERRIS). We evaluated the performance of the operational rainfall and streamflow forecasts for 96 catchments using four years of operational data between January 2020 and December 2023. Performance evaluation metrics included the following: CRPS, relative CRPS, CRPSS, and PIT-Alpha for ensemble forecasts; NSE, PCC, MAE, KGE, PBias, and RMSE; and three categorical metrics, CSI, FAR, and POD, for deterministic forecasts. The skill scores, CRPS, relative CRPS, CRPSS, and PIT-Alpha, gradually decreased for both rainfall and streamflow as the forecast horizon increased from Day 1 to Day 7. A similar pattern emerged for NSE, KGE, PCC, MAE, and RMSE as well as for the categorical metrics. Forecast performance also progressively decreased with higher streamflow volumes. Most catchments showed positive performance skills, meaning the ensemble forecast outperformed climatology. Both streamflow and rainfall forecast skills varied spatially across the country—they were generally better in the high-runoff-generating catchments, and poorer in the drier catchments situated in the western part of the Great Dividing Range, South Australia, and the mid-west of Western Australia. We did not find any association between the model forecast skill and the catchment area. Our findings demonstrate that the 7-day ensemble streamflow forecasting service is robust and draws great confidence from agencies that use these forecasts to support decisions around water resource management.

Accelerating Lagrangian transport simulations on graphics processing units: performance optimizations of Massive-Parallel Trajectory Calculations (MPTRAC) v2.6

Abstract. Lagrangian particle dispersion models are indispensable tools for the study of atmospheric transport processes. However, Lagrangian transport simulations can become numerically expensive when large numbers of air parcels are involved. To accelerate these simulations, we made considerable efforts to port the Massive-Parallel Trajectory Calculations (MPTRAC) model to graphics processing units (GPUs). Here we discuss performance optimizations of the major bottleneck of the GPU code of MPTRAC, the advection kernel. Timeline, roofline, and memory analyses of the baseline GPU code revealed that the application is memory-bound, and performance suffers from near-random memory access patterns. By changing the data structure of the horizontal wind and vertical velocity fields of the global meteorological data driving the simulations from structure of arrays (SoAs) to array of structures (AoSs) and by introducing a sorting method for better memory alignment of the particle data, performance was greatly improved. We evaluated the performance on NVIDIA A100 GPUs of the Jülich Wizard for European Leadership Science (JUWELS) Booster module at the Jülich Supercomputing Center, Germany. For our largest test case, transport simulations with 108 particles driven by the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis, we found that the runtime for the full set of physics computations was reduced by 75 %, including a reduction of 85 % for the advection kernel. In addition to demonstrating the benefits of code optimization for GPUs, we show that the runtime of central processing unit (CPU-)only simulations is also improved. For our largest test case, we found a runtime reduction of 34 % for the physics computations, including a reduction of 65 % for the advection kernel. The code optimizations discussed here bring the MPTRAC model closer to applications on upcoming exascale high-performance computing systems and will also be of interest for optimizing the performance of other models using particle methods.

Sun from Behind the Clouds: the Appeals Board of the European Centre for Medium-Range Weather Forecasts

Abstract One of the lesser-known regional international organizations in Europe is the European Centre for Medium-Range Weather Forecasts (ECMWF). It has a mandate to undertake tasks such as the production of weather forecasts, conduct scientific research to improve meteorological projections, maintain extensive datasets, and to better coordinate European meteorological infrastructure. The Centre has three sites across three European states where it has over 400 staff, and given it is immune from suit, its staff members only have an internal justice mechanism to resort to in the event of employment/pensions disputes. Little is known about the ECMWF Appeals Board given its untransparent nature. As uncovered in this article, it has undergone partial reform, and simultaneously, there has been an uptick in the number of cases. This article analyses these developments and highlights that more must be done to align the ECMWF Appeals Boards with the features of other international administrative tribunals.

Using CloudSat to Advance the Global Precipitation Climatology Project (GPCP) over Antarctica

Remote sensing-based precipitation products face several challenges in high latitudes and specifically over frozen surfaces (i.e., snow and ice). Consequently, precipitation estimates tend to be lower in quality over these regions, including Antarctica, the coldest continent on Earth. In this study, we developed a method for adjusting precipitation estimates over Antarctica by leveraging CloudSat's ability to capture snowfall compared to other satellite products over snow and ice surfaces. We addressed limitations of CloudSat, such as poor spatiotemporal sampling, noise, and incomplete coverage near the poles. We utilized the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) to guide development, particularly during the period when the TIROS Operational Vertical Sounder (TOVS) was contributing. We adjusted monthly and seasonal TOVS and Atmospheric Infrared Sounder – Infrared (AIRS-IR) precipitation biases over Antarctica at the pixel level, as these two products are the primary inputs for the Global Precipitation Climatology Project (GPCP) in high latitudes. We assessed the adjusted TOVS and AIRS-IR through analyses of geographical maps and time series of monthly and seasonal mean precipitation rates. The results are encouraging, indicating that the proposed approach could replace the current approach used in the GPCP for adjusting precipitation estimates from AIRS-IR and TOVS over Antarctica. Adjusting precipitation estimates from TOVS and AIRS-IR using the proposed approach improves the Kling-Gupta efficiency (KGE) over their entire period by 162% and 147%, respectively. Moreover, the proposed approach can be applied to adjust other precipitation products over Antarctica at the pixel level, including satellites and reanalysis products.