European Centre For Medium-Range Weather Forecasts Research Articles

Assimilating FY-3D MWHS2 Radiance Data to Predict Typhoon Muifa Based on Different Initial Background Conditions and Fast Radiative Transfer Models

In this study, the impact of assimilating MWHS2 radiance data under different background conditions on the analyses and deterministic prediction of the Super Typhoon Muifa case, which hit China in 2022, was explored. The fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) data and the Global Forecast System (GFS) analysis data from the National Centers for Environmental Prediction (NCEP) were used as the background fields. To assimilate the Microwave Humidity Sounder II (MWHS2) radiance data into the numerical simulation experiments, the Weather Research and Forecasting (WRF) model and its three-dimensional variational data assimilation system were employed. The results show that after the data assimilation, the standard deviation and root-mean-square error of the analysis significantly decrease relative to the observation, indicating the effectiveness of the assimilation process with both background fields. In the MWHS_GFS experiment, a subtropical high-pressure deviation to the east is observed around the typhoon, resulting in its northeast movement. In the differential field of the MWHS_ERA experiment, negative sea-level pressure differences around the typhoon are observed, which increases its intensity. In the deterministic predictions, assimilating the FY3D MWHS2 radiance data reduces the typhoon track error in the MWHS_GFS experiment and the typhoon intensity error in the MWHS_ERA experiment. In addition, it is found that the Community Radiative Transfer Model (CRTM) and the Radiative Transfer for Tovs (RTTOV) model show similar performance in assimilating MWHS2 radiance data for this typhoon case. It seems that the data assimilation experiment with the CRTM significantly reduces the typhoon track error than the experiment with the RTTOV model does, while the intensity error of both experiments is rather comparable.

An integrated method for extended-range prediction of heavy Precipitation process in the flood season over Hunan Province based on S2S models

Abstract Floods in the middle reaches of Yangtze River threaten millions of people and cause casualties and economic losses. Yet, the prediction of floods especially on the sub-seasonal scale in this region is still challenging. To better predict the floods during the flood season (from April to August) in Hunan Province, the models from the China Meteorological Administration (CMA), the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP) that participated in the sub-seasonal to seasonal (S2S) prediction project were chosen to evaluate their extended-range (the next 11–30 days) prediction skills for heavy precipitation. The original prediction score of single model (original score), the score of single model using optimal threshold of heavy precipitation (adjusted score) and the score of multi-model integration (integrated score) were calculated by the scoring rules for heavy precipitation process. The results show that the integrated score in the extended-range is 75.1, which is 10.3 and 6.9 higher than the average scores of original models and adjusted method, respectively. The false alarm (missing) rate of the integrated method is 5.4% (33.9%), which is 8.6% (4.7%) and 2.9% (3.3%) smaller than the average rates of original models and adjusted method, respectively.

Performance evaluation of CMIP6 HighResMIP models in simulating precipitation over Madagascar

AbstractThe present study evaluates the performance of high‐resolution global climate models derived from Coupled Model Intercomparison Project Phase 6 (CMIP6 HighResMIP), in simulating rainfall characteristics over Madagascar on an annual and seasonal scales for the period 1981–2014. The models and their ensemble mean are assessed based on two observational datasets sourced from Climate Hazards Group Infrared Precipitation with Station data version 2 (CHIRPS v2.0) data and the European Centre for Medium‐Range Weather Forecasts (ECMWF) reanalysis fifth generation‐Land dataset (ERA5) as the references throughout the diverse analyses. A Taylor diagram, accompanied by the Taylor skill score (TSS), is used for the annual and seasonal model‐rankings and the overall performance of the models. The best‐performing models are EC‐Earth3P‐HR, ECMWF‐IFS‐HR, ECMWF‐IFS‐LR and HadGEM3‐GC31‐MM. The least‐recommended models with remarkable biases are BCC‐CSM2‐HR, CAMS‐CSM1‐0, FGOALS‐f3‐H, MPI‐ESM1‐2‐HR and MPI‐ESM1‐2‐XR. It is worth mentioning that FGOALS‐f3‐H tends to overestimate rainfall in most analyses, while MPI‐ESM1‐2‐HR and MPI‐ESM1‐2‐XR underestimate it. The findings of this study are of great importance to climatologists and present an opportunity for further investigation of underlying processes responsible for the observed wet/dry biases in order to improve the forecast skills in the models over the study area.

Adaptive bias correction for improved subseasonal forecasting

Subseasonal forecasting—predicting temperature and precipitation 2 to 6 weeks ahead—is critical for effective water allocation, wildfire management, and drought and flood mitigation. Recent international research efforts have advanced the subseasonal capabilities of operational dynamical models, yet temperature and precipitation prediction skills remain poor, partly due to stubborn errors in representing atmospheric dynamics and physics inside dynamical models. Here, to counter these errors, we introduce an adaptive bias correction (ABC) method that combines state-of-the-art dynamical forecasts with observations using machine learning. We show that, when applied to the leading subseasonal model from the European Centre for Medium-Range Weather Forecasts (ECMWF), ABC improves temperature forecasting skill by 60–90% (over baseline skills of 0.18–0.25) and precipitation forecasting skill by 40–69% (over baseline skills of 0.11–0.15) in the contiguous U.S. We couple these performance improvements with a practical workflow to explain ABC skill gains and identify higher-skill windows of opportunity based on specific climate conditions.

A multi-scale algorithm for the NISAR mission high-resolution soil moisture product

This study proposes a multi-scale soil moisture algorithm for the upcoming NASA-ISRO SAR (NISAR) mission to estimate high-resolution (200 [m]) soil moisture (the water content of the soil). The algorithm takes advantage of the high-resolution (∼10 [m]) synthetic aperture radar (SAR) backscatter and coarse resolution modeled/reanalysis soil moisture products (∼ 9 [km]) to create a high-resolution (200 [m]) soil moisture product at a global extent. The end goal of the algorithm is to remove dependencies on any complex modeling, tedious retrieval steps, or multiple ancillary data needs, and subsequently decrease the degrees of freedom to achieve optimal accuracy in soil moisture retrievals. The use of modeled/reanalysis soil moisture products with high temporal resolution gives an added advantage in reducing the temporal mismatch between the two different inputs used in the algorithm. In this study, the proposed algorithm is tested using L-band UAVSAR backscatter (σ°) data and Advanced Land Observing Satellite −2 (ALOS-2) SAR σ° as a substitute for the NISAR L-band SAR observations. The algorithm uses the L-band SAR σ° to disaggregate coarse resolution (∼9 [km]) reanalysis soil moisture of the European Centre for Medium-Range Weather Forecast (ECMWF) to a high-resolution of ∼200 [m] soil moisture product. The potential of the algorithm is demonstrated over three sites in different hydroclimatic regions of the world, such as India, the USA, and Canada. The high-resolution soil moisture estimates were compared with the in-situ soil moisture measurements available for three sites (North India, Southern California, and Carman, Manitoba, Canada). In North India, in-situ measurements are from paddy crops with high vegetation water content, the unbiased root-mean-square-error (ubRMSE) for the high-resolution soil moisture retrievals was found to be 0.036 [m3/m3] with a bias of −0.051 [m3/m3]. For the southern California site, the validation statistics shows low ubRMSE of 0.027 [m3/m3] and a low bias of 0.016 [m3/m3]. At the Carman, Manitoba test site of Canada, where in-situ soil measurements were available for multiple crop types, the comparison shows that the ubRMSE for all the crop types lies below 0.05 [m3/m3] with an average bias of <0.07 [m3/m3]. The result confirms that the proposed algorithm meets the NISAR mission's accuracy goals, i.e., 0.06 [m3/m3] ubRMSE over areas with vegetation water content (VWC) below 5 [kg/m2]. Rigorous validation work will still need to be carried out in the future based on the availability of L-band SAR datasets and after the launch of the NISAR satellite.

Vertical information of CO from TROPOMI total column measurements in context of the CAMS-IFS data assimilation scheme

Abstract. Since 2017 the Tropospheric Monitoring Instrument (TROPOMI) on board ESA's Copernicus Sentinel-5 satellite (S5-P) has provided the operational carbon monoxide (CO) data product with daily global coverage on a spatial resolution of 5.5×7 km2. The European Centre for Medium-Range Weather Forecasts (ECMWF) plans to assimilate the retrieved total columns and the corresponding vertical sensitivities in the Copernicus Atmosphere Monitoring Service Integrated Forecasting System (CAMS-IFS) to improve forecasts of the atmospheric chemical composition. The TROPOMI data will primarily constrain the vertical integrated CO field of CAMS-IFS but to a lesser extent also its vertical CO distribution. For clear-sky conditions, the vertical sensitivity of the TROPOMI CO data product is useful throughout the atmosphere, but for cloudy scenes it varies due to cloud shielding and light scattering. To assess the profile information, we deploy an a posteriori profile retrieval that combines an ensemble of TROPOMI CO column retrievals with different vertical sensitivities to obtain a vertical CO profile that is then a representative average for the chosen spatial and temporal domain. We demonstrate the approach on three CO pollution cases. For the so-called “Rabbit Foot Fire” in Idaho on 12 August 2018, we estimate a CO profile showing the pollution at an altitude of about 5 km in good agreement with airborne in situ measurements of the Biomass Burning Flux Measurements of Trace Gases and Aerosol (BB-FLUX) field campaign. The distinct CO enhancement in a plume aloft (length =212 km, width =34 km), decoupled from the ground, is sensed by TROPOMI but is not present in the CAMS-IFS model. For a large-scale event, we analyzed the CO pollution from Siberian wildfires that took place from 14 to 17 August 2018. The TROPOMI data estimate the height of the pollution plume over Canada at 7 km in agreement with CAMS-IFS. However, CAMS-IFS underestimates the enhanced CO vertical column densities sensed by TROPOMI within the plume by more than 100 ppb. Finally, we study the seasonal biomass burning in the Amazon. During the burning season the CO profile retrieved from the TROPOMI measurements (1–15 August 2019) agrees well with the one of CAMS-IFS with a similar vertical shape between ground and 14 km altitude. Hence, our results indicate that assimilating TROPOMI CO retrieval with different vertical sensitivities (e.g., under clear-sky and cloudy conditions) provides information about the vertical distribution of CO.

Hydrological regime of Sahelian small waterbodies from combined Sentinel-2 MSI and Sentinel-3 Synthetic Aperture Radar Altimeter data

Abstract. In the Sahelian semi-arid region, water resources, especially small waterbodies such as ponds, small lakes, and reservoirs in rural areas are of vital importance. However, because of their high number and the scarce in situ monitoring networks, these resources and their spatiotemporal variability are not well known at the regional scale. This study investigates the hydrological regime of 37 small waterbodies, located in Mali, Niger, and Burkina Faso in central Sahel. We propose a method based on remote sensing data only, which consists of combining water height data from Sentinel-3 Synthetic Aperture Radar Altimeter (SRAL) with water area data obtained with the Sentinel-2 MultiSpectral Instrument (MSI) to create a dense water height time series. Water height variations are then compared to the evaporation estimated by the Penman–Monteith method, using ERA5 reanalysis by the European Centre for Medium-Range Weather Forecasts (ECMWF) to infer water regimes during the dry season. Three main regimes stand out, namely a net water loss, mainly resulting from anthropogenic withdrawals, a net water supply occurring after the end of the rainy season through river network or water table exchange, and a balanced behaviour, where water losses during the dry season closely correspond to evaporation rates. Spatial patterns have been identified; in central Burkina Faso, most of the reservoirs show a net dry season water loss, which is explained by frequent irrigation, while reservoirs in northern Burkina Faso, generally show little water loss, indicating that water withdrawal is not significant in this area. Lakes located in the Inner Niger Delta in Mali and connected to the Niger River network generally show an important water supply, particularly at the beginning of the dry season. Lakes in Niger tend to show a weak signal toward water inflow that could be explained by exchange processes with the groundwater. These results show that satellite data are effective at estimating hydrological regimes and the anthropogenic impact on water resources at the large scale, including resources found in small waterbodies.

Post-processing quantitative precipitation forecasts using the seasonally coherent calibration model

ABSTRACT Skilful precipitation ensemble forecasts are necessary to produce trustworthy hydrologic predictions. Raw quantitative precipitation forecasts (QPFs) from the numerical weather prediction (NWP) models are known to be error-prone. In this study, sub-basin averaged deterministic QPFs with five-day lead times from the European Centre for Medium-Range Weather Forecasts (ECMWF) are post-processed through the Seasonally Coherent Calibration (SCC) model for the Narmada and Godavari River basins of India. The SCC model incorporates seasonal climatology from long observations into forecasts and produces calibrated forecasts based on a joint probability model. The SCC model results are compared with the post-processed forecasts from the state-of-the-art Quantile Mapping (QM) method. The results suggest that the probabilistic ensemble forecasts generated from the SCC model have improved skill throughout five-day lead times. Further, the application of SCC-calibrated precipitation forecasts is demonstrated using the Soil & Water Assessment Tool (SWAT) to generate streamflow forecasts.

Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System: RRTMGP-NN 2.0

Abstract. Radiation schemes are physically important but computationally expensive components of weather and climate models. This has spurred efforts to replace them with a cheap emulator based on neural networks (NNs), obtaining large speed-ups, but at the expense of accuracy, energy conservation and generalization. An alternative approach, which is slower but more robust than full emulation, is to use NNs to predict optical properties but keep the radiative transfer equations. Recently, NNs were developed to replace the RRTMGP (Rapid Radiative Transfer Model for General circulation model applications–Parallel) gas optics scheme and shown to be accurate while improving speed. However, the evaluations were based solely on offline radiation computations. In this paper, we describe the implementation and prognostic evaluation of RRTMGP-NN in the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). The new gas optics scheme was incorporated into ecRad, the modular ECMWF radiation scheme. Using two new methods to improve accuracy – a hybrid loss function designed to reduce radiative forcing errors and an early stopping method based on monitoring fluxes and heating rates with respect to a line-by-line benchmark – we train NN models on RRTMGP k distributions with reduced spectral resolutions. Offline evaluation of the new NN gas optics, RRTMGP-NN 2.0, shows a very high level of accuracy for clear-sky fluxes and heating rates. For instance, the RMSE in the shortwave surface downwelling flux is 0.78 W m−2 for RRTMGP and 0.80 W m−2 for RRTMGP-NN in a present-day scenario, while upwelling flux errors are actually smaller for the NN. Because our approach does not affect the treatment of clouds, no additional errors will be introduced for cloudy profiles. RRTMGP-NN closely reproduces radiative forcings for five important greenhouse gases across a wide range of concentrations such as 8×CO2. To assess the impact of different gas optics schemes in the IFS, four 1-year coupled ocean–atmosphere simulations were performed for each configuration. The results show that RRTMGP-NN and RRTMGP produce very similar model climates, with the differences being smaller than those between existing schemes and statistically insignificant for zonal means of single-level quantities such as surface temperature. The use of RRTMGP-NN speeds up ecRad by a factor of 1.5 compared to RRTMGP (the gas optics being almost 3 times faster) and is also faster than the older and less accurate RRTMG, which is used in the current operational cycle of the IFS.

East Asia Reanalysis System (EARS)

Abstract. Reanalysis data play a vital role in weather and climate study as well as meteorological resource development and application. In this work, the East Asia Reanalysis System (EARS) was developed using the Weather Research and Forecasting (WRF) model and the Gridpoint Statistical Interpolations (GSI) data assimilation system. The regional reanalysis system is forced by the European Centre for Medium-Range Weather Forecasts (ECMWF) global reanalysis ERA-Interim data at 6 h intervals. Hourly surface observations are assimilated by the Four-Dimension Data Assimilation (FDDA) scheme during the WRF model integration; upper observations are assimilated in three-dimensional variational data assimilation (3D-VAR) mode at the analysis moment. It should be highlighted that many of the assimilated observations have not been used in other reanalysis systems. The reanalysis runs from 1980 to 2018, producing a regional reanalysis dataset covering East Asia and surrounding areas at 12 km horizontal resolution, 74 sigma levels, and 3 h intervals. Finally, an evaluation of EARS has been performed with respect to the root mean square error (RMSE), based on the 10-year (2008–2017) observational data. Compared to the global reanalysis data of ERA-Interim, the regional reanalysis data of EARS are closer to the observations in terms of RMSE in both surface and upper-level fields. The present study provides evidence for substantial improvements seen in EARS compared to the ERA-Interim reanalysis fields over East Asia. The study also demonstrates the potential use of the EARS data for applications over East Asia and proposes further plans to provide the latest reanalysis in real-time operation mode. Simple data and updated information are available on Zenodo at https://doi.org/10.5281/zenodo.7404918 (Yin et al., 2022), and the full datasets are publicly accessible on the Data-as-a-Service platform of the China Meteorological Administration (CMA) at http://data.cma.cn (last access: 19 May 2023).

Shipborne GNSS reflectometry for monitoring along-track significant wave height and wind speed

Shipborne Global Navigation Satellite System-Reflectometry (GNSS-R) for measuring along-track significant wave height (SWH) and wind speed are first investigated in this article. A real-time shipborne GNSS-R instrument was carried on the XUELONG-2 for the 38th Antarctic Scientific Expedition. The collected dataset covered the entire expedition of the XUELONG-2. The dependencies of the correlation time, normalized area, ratio of reflected-to-direct signal-to-noise ratio (SNR), spectral width, and peak frequency on SWH and wind speed are explored. It is found that the correlation time, spectral width, and peak frequency better depend on SWH and wind speed than other two observables, therefore, they are used to measure the SWH and wind speed. The elevation angle, SNR and ship velocity, also impact on the observables, thus their influences should be corrected. The geophysical model function (GMF) and neural network are used to develop retrieval models of SWH and wind speed. Neural networks provide better retrieval performances. The best root mean square errors (RMSEs) of the retrieved SWH and wind speed are 0.38 m and 2.49 m/s, with a temporal and spatial resolutions of 1 min and 600 m, respectively. The measurements from the shipborne GNSS-R also agree with the co-located European Centre for Medium-Range Weather Forecasts (ECMWF) and Jason-3 altimeter data.

Cut-cell Eta Model: History and Challenges Overcome

Incentive for writing a limited area weather prediction model stemmed from the author’s several years stay at the University of California in Los Angeles, at the end of the sixties. Exposed to what he refers to as the Akio Arakawa approach, having had an idea for a scheme that was an improvement to what Arakawa was using, and being aware of the importance of topography for the weather of the country he was to continue his career in, led in 1973 to his first limited area 3D code, the forerunner of what was to become the Eta model. Refinements and enhancements introduced by the author in subsequent years and of the collaborator he acquired, Zaviša Janjić, resulted in the code that when installed at the then U.S. National Meteorological Center, attracted attention. Hallmarks of the model were Mesinger’s eta vertical coordinate, and Janjić’s transformation of the Arakawa horizontal advection scheme to the model’s semi-staggered B/E grid. In 1993 the Eta became the primary regional forecasting model of the U.S. Weather Bureau, and in 1998 its precipitation accuracy of 24-48 h forecasts became higher across all intensity thresholds than that of its predecessor, the Nested Grid Model (NGM) for its 00-24 h forecasts. Lately, the Eta is extensively used also as a regional climate model (RCM), mostly over the South American domain, and in near-real time as a tool for the North American Regional Reanalysis (NARR), run by the U.S. National Centers for Environmental Prediction/Climate Prediction Center. Several later unique numerical refinements of the Eta addressing problems noticed are summarized in a “before and after” fashion, and results are mentioned of its ensemble skill compared to that of its highly acclaimed driver European Centre for Medium-Range Weather Forecasts (ECMWF) model.

Temperature Forecasting as a Means of Mitigating Climate Change and Its Effects: A Case Study of Mali

Temperature forecasts and trend analyzes were carried out for several locations in Mali as an important tool for warning of potentially threatening weather events such as severe heat waves, storms, droughts and floods, which could pose a great risk to humans and their environment. Five locations (Segou, Sikasso, Kayes, Gao and Taoudenni) across Mali (170 00’N – 40 00’W) were chosen for this research work. Satellite data of annual temperature obtained from the European Centre for Medium-Range Weather Forecast (ECMWF) database for 35 years (1985-2019) was used for this work. The Mann-Kendall trend test was carried out for various locations to observe and study the trend. Four Models including Auto Regressive and Integrated Moving Average (ARIMA), Exponential smoothening (ETS), TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal components) and the linear model were employed to forecast average temperature for 10 years for all the locations. The model that produces the best forecast at the 95% confidence level is expected to have the lowest Root Mean Square Error (RMSE) value. The results showed that no significant trends were recorded at the considered locations. The linear model produced the best forecast for Segou, Kayes and Taoudenni, while the TBATS model produced the best forecast for Gao and the ARIMA model produced the best forecast for Sikasso.

Identification of temperature change related to frost phenomenon over Jayapura, Indonesia

The frost phenomenon that occurred over Jayapura, Indonesia, in July 2022 was the worst frost in the last two decades (1998 – 2022). The frost impact destroyed agricultural fields, crippling most of the crops, and caused famishing to hundreds of people. Because Jayapura has the largest agriculture field in Papua, an assessment of possible mechanisms that caused this extreme event is needed for disaster mitigation. On the other hand, numerous previous studies investigated the relationship between frost and climate change. However, it is still poorly understood, particularly in the Papua study case. This study explores the linkage between the frost phenomenon and climate change in Jayapura, Papua. Hence, we conducted an analysis using station observation and reanalysis datasets. In addition, we also assess spatial analysis to determine the distribution of weather pattern anomalies derived from The European Center for Medium-Range Weather Forecast (ECMWF) – ERA5 datasets during extreme episodes. We found that maximum and average temperatures have increased, while the minimum temperature has decreased over the last decade (2002-2022). It is also noted that the frost event may be related to temperature change in Jayapura, Papua.

Data-driven aeolian dust emission scheme for climate modelling evaluated with EMAC 2.55.2

Abstract. Aeolian dust has significant impacts on climate, public health, infrastructure and ecosystems. Assessing dust concentrations and the impacts is challenging because the emissions depend on many environmental factors and can vary greatly with meteorological conditions. We present a data-driven aeolian dust scheme that combines machine learning components and physical equations to predict atmospheric dust concentrations and quantify the sources. The numerical scheme was trained to reproduce dust aerosol optical depth retrievals by the Infrared Atmospheric Sounding Interferometer on board the MetOp-A satellite. The input parameters included meteorological variables from the fifth-generation atmospheric reanalysis of the European Centre for Medium-Range Weather Forecasts. The trained dust scheme can be applied as an emission submodel to be used in climate and Earth system models, which is reproducibly derived from observational data so that a priori assumptions and manual parameter tuning can be largely avoided. We compared the trained emission submodel to a state-of-the-art emission parameterisation, showing that it substantially improves the representation of aeolian dust in the global atmospheric chemistry–climate model EMAC.

Assessment of GNSS zenith tropospheric delay responses to atmospheric variables derived from ERA5 data over Nigeria

Tropospheric delay is a major error caused by atmospheric refraction in Global Navigation Satellite System (GNSS) positioning. The study evaluates the potential of the European Centre for Medium-range Weather Forecast (ECMWF) Reanalysis 5 (ERA5) atmospheric variables in estimating the Zenith Tropospheric Delay (ZTD). Linear regression models (LRM) are applied to estimate ZTD with the ERA5 atmospheric variables. The ZTD are also estimated using standard ZTD models based on ERA5 and Global Pressure and Temperature 3 (GPT3) atmospheric variables. These ZTD estimates are evaluated using the data collected from the permanent GNSS continuously operating reference stations in the Nigerian region. The results reveal that the Zenith Hydrostatic Delay (ZHD) from the LRM and the Saastamoinien model using ERA5 surface pressure are of identical accuracy, having a Root Mean Square (RMS) error of 2.3 mm while the GPT3-ZHD has an RMS of 3.4 mm. For the Zenith Wet Delay (ZWD) component, the best estimates are derived using ERA5 Precipitable Water Vapour (PWV). These include the ZWD derived by the LRM having an average RMS of 20.9 mm and Bevis equation having RMS of 21.1 mm and 21.0 mm for global and local weighted mean temperatures, respectively. The evaluation of GPT3-ZWD estimates gives RMS of 45.8 mm. This study has provided a valuable insight into the application of ERA5 data for ZTD estimation. In line with the findings of the study, the ERA5 atmospheric variables are recommended for improving the accuracy in ZTD estimation, required for GNSS positioning.

Assessing and Comparing Reference Evapotranspiration across Different Climatic Regions of China Using Reanalysis Products

This study aims to assess the accuracy of the reference evapotranspiration (ET0) estimated by CLDAS, ERA5 reanalysis products, and the quality of reanalysis weather variables required to calculate PM-ET0. For this purpose, the applicability of surface meteorological elements from the ERA5 reanalysis datasets provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), and the second-generation China Meteorological Administration Land Data Assimilation System (CLDASV2.0) datasets are evaluated in China by comparison with local observations from 689 stations reported by the Chinese Meteorological Administration (CMA). Statistics including percent bias (PBias), coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) are used to check the accuracy. The results show the highest correlation between reanalysis temperature and observations, with a mean R2 of 0.96, 0.90 for the CLDAS maximum and minimum air temperatures, and 0.87, 0.84 for ERA5. For the reanalysis of solar radiation (Rs) and relative humidity (RH), an overestimation trend is shown for Rs and an underestimation trend is shown for RH. For reanalysis of wind speed, a relatively low accuracy is shown. The accuracy of ET0 estimated by the two reanalysis products is acceptable in China, but the spatial and temporal consistency between the CLDAS estimates and site observations is higher, with a mean RMSE R2 of 0.91, 0.82 for CLDAS and 1.42, 0.70 for ERA5, respectively. Moreover, CLDAS reanalysis products are more effective in describing the boundary details of the study area.

Sea level variation in the Arctic Ocean since 1979 based on ORAS5 data

The Arctic is currently experiencing unprecedented changes across all components of the climate system, primarily driven by global warming. As an important indicator of climate change in the Arctic, sea level reflects variations in both the atmosphere and ocean. This paper analyzes the sea level variation of the Arctic Ocean over the past four decades using ORAS5 data, which is the product of the latest reanalysis-analysis system produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). ORAS5 accurately reproduces the main spatial features of the climatology and temporal evolution of sea surface height (SSH) in the Arctic Ocean, as observed by satellite altimeters, and reveals that seasonal variability is the most significant property of the sea level variation in this region. The seasonal cycle of SSH is closely linked to atmospheric circulation and sea ice formation. The first two dominant modes of the annual-mean SSH in the Arctic Ocean exhibit significant decadal variability. The first mode can be explained by the Ekman transport of wind related to the Arctic Oscillation (AO), which leads to antiphase changes in SSH on the continental shelves and in the deep basins. The second mode shows an antiphase oscillation of SSH between the Eurasian and Canadian Arctic Archipelago (CAA) sides and is driven by the wind anomaly associated with the Arctic dipole anomaly (DA). Due to the decadal variations associated with climate modes, particularly the AO, sea level in the Arctic Ocean has been continuously rising since the mid-1990s or early 2000s, with the most rapid sea level rise occurring in the Beaufort Sea.

Wave and Meso-Scale Eddy Climate in the Arctic Ocean

Under global climate change, the characteristics of oceanic dynamics are gradually beginning to change due to melting sea ice. This study focused on inter-annual variation in waves and mesoscale eddies (radius &gt; 40 km) in the Arctic Ocean from 1993 to 2021. The waves were simulated by a numerical wave model, WAVEWATCH-III (WW3), which included a parameterization of ice–wave interaction. The long-term wind data were from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-5), and current and sea level data from the HYbrid Coordinate Ocean Model (HYCOM)were used as the forcing fields. The simulated significant wave heights (SWHs) were validated against the 2012 measurements from the Jason-2 altimeter, yielding a 0.55 m root mean square error (RMSE) with a 0.95 correlation (COR). The seasonal variation in WW3-simulated SWH from 2021 to 2022 showed that the SWH was the lowest in summer (July and August 2021) and highest in winter (November 2021 to April 2022). This result indicates that parts of the Arctic could become navigable in summer. The mesoscale eddies were identified using a daily-averaged sea level anomalies (SLA) product with a spatial resolution of a 0.25° grid for 1993−2021. We found that the activity intensity (EKE) and radius of mesoscale eddies in the spatial distribution behaved in opposing ways. The analysis of seasonal variation showed that the increase in eddy activity could lead to wave growth. The amplitude of SWH peaks was reduced when the Arctic Oscillation Index (AOI) was &lt;−1.0 and increased when the AOI was &gt;0.5, especially in the case of swells. The amplitude of SWH oscillation was low, and the EKE and radius of eddies were relatively small. Although the radius and EKE of eddies were almost similar to the AOI, the waves also influenced the eddies.

Exploration of dual-attention mechanism-based deep learning for multi-step-ahead flood probabilistic forecasting

In order to improve the flood forecasting accuracy and reflect the forecast uncertainty information in the Three Gorges Reservoir (TGR) interval-basin in China, this study integrates the feature and temporal dual-attention (DA) mechanism and recursive encoder-decoder (RED) structure into the long short-term memory (LSTM) neural network to develop a DA-LSTM-RED model. The feature attention acts on the input variables of the encoder, and the temporal attention mechanism acts on the hidden layer states extracted by the LSTM neural network during encoding process, prompting the proposed model to extract critical input information among different types and moments of input variables to improve the multi-step-ahead flood forecasting accuracy. Second, the copula-based Hydrological Uncertainty Processor (copula-HUP) is used to quantify the forecast uncertainty of the proposed model meanwhile creating multi-step-ahead flood probabilistic forecasts. Combining the long-term 6 h hydrologic data series of the Xiangjiaba-TGR interval-basin and the forecasted precipitation from the European Centre for Medium-Range Weather Forecasts (ECMWF), the effectiveness of the proposed model, the effect of forecast precipitation on multi-step-ahead flood forecasting, and the effect of different copula functions on the probabilistic forecast of copula-HUP are investigated, respectively. The results show that the DA-LSTM-RED model can effectively improve the forecasting accuracy for long forecast horizons (3-7d) compared to the LSTM-RED model, and the average absolute error metrics are reduced by 10%-17%. Meanwhile, the proposed model can identify input variables with a high correlation with the target output variables, which improves the interpretability of deep learning to a certain extent. The Student copula-HUP has the lowest RB and CRPS metrics than the Frank and Gaussian copula-HUP, which can better quantify the DA-LSTM-RED model's forecast uncertainty. Therefore, combining the proposed model with the Student copula-HUP can effectively reduce the forecasting uncertainty, enhance the forecasting reliability and accuracy for future horizons, and provide reliable risk information for the TGR flood control scheduling decision.