Centre forMedium-Range Weather Forecasts Research Articles

Combining quantiles of calibrated solar forecasts from ensemble numerical weather prediction

This work is concerned with optimally combining quantiles of several post-processed versions of ensemble solar forecasts, which is new in this field. Numerical weather prediction (NWP) serves grid integration of solar energy by issuing dynamical ensemble irradiance forecasts. However, these ensemble members often suffer from under-dispersion, which motivates statistical calibration via quantile regression (QR) or ensemble model output statistics (EMOS). Given the numerous variants of QR and EMOS, it is generally unclear which variant offers the best performance under what situation, which further motivates combining quantile forecasts. A framework for combining solar forecasts in the form of quantiles is proposed, and a constrained quantile regression averaging scheme is used to exemplify the framework. Using the strictly proper pinball loss, ensemble irradiance forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System are first post-processed using five QR variants and five EMOS variants, and then combined through a linear program. It is found that combining quantiles is an effective strategy that can further improve the calibrated ECMWF forecasts across all locations herein considered.

Accurate medium-range global weather forecasting with 3D neural networks

Weather forecasting is important for science and society. At present, the most accurate forecast system is the numerical weather prediction (NWP) method, which represents atmospheric states as discretized grids and numerically solves partial differential equations that describe the transition between those states1. However, this procedure is computationally expensive. Recently, artificial-intelligence-based methods2 have shown potential in accelerating weather forecasting by orders of magnitude, but the forecast accuracy is still significantly lower than that of NWP methods. Here we introduce an artificial-intelligence-based method for accurate, medium-range global weather forecasting. We show that three-dimensional deep networks equipped with Earth-specific priors are effective at dealing with complex patterns in weather data, and that a hierarchical temporal aggregation strategy reduces accumulation errors in medium-range forecasting. Trained on 39 years of global data, our program, Pangu-Weather, obtains stronger deterministic forecast results on reanalysis data in all tested variables when compared with the world’s best NWP system, the operational integrated forecasting system of the European Centre for Medium-Range Weather Forecasts (ECMWF)3. Our method also works well with extreme weather forecasts and ensemble forecasts. When initialized with reanalysis data, the accuracy of tracking tropical cyclones is also higher than that of ECMWF-HRES.

Uncertainty growth and forecast reliability during extratropical cyclogenesis

Abstract. In global numerical weather prediction, the strongest contribution to ensemble variance growth over the first few days is at synoptic scales. Hence it is particularly important to ensure that this synoptic-scale variance is reliable. Here we focus on wintertime synoptic-scale growth in the North Atlantic storm track. In the 12 h background forecasts of the Ensemble of Data Assimilations (EDA) from the European Centre for Medium-Range Weather Forecasts (ECMWF), we find that initial variance growth at synoptic scales tends to be organized in particular flow situations, such as during the deepening of cyclones (cyclogenesis). Both baroclinic and diabatic aspects may be involved in the overall growth rate. However, evaluation of reliability through use of an extended error–spread equation indicates that the ECMWF ensemble forecast, which is initialized from the EDA but with additional singular vector perturbations, appears to have too much variance at a lead time of 2 d and that this over-spread is associated with cyclogenesis situations. Comparison of variance growth rates and reliability with other forecast systems within The International Grand Global Ensemble (TIGGE) archive indicates some sensitivity to the model or its initialization. For the ECMWF ensemble forecast, sensitivity experiments suggest that a large part of the total day-2 spread in cyclogenesis cases is associated with the growth of EDA uncertainty, but up to 25 % can be associated with the additional singular vector perturbations to the initial conditions and up to 25 % with the representation of model uncertainty. The sensitivities of spread to resolution, the explicit representation of convection, and the assimilation of local observations are also considered. The study raises the question of whether the EDA now successfully represents initial uncertainty (and the enhanced growth rates associated with cyclogenesis) to the extent that singular vector perturbations could be reduced in magnitude to improve storm track reliability. This would leave a more seamless forecast system, allowing short-range diagnostics to better help improve the model and model-uncertainty representation, which could be beneficial throughout the forecast range.

Ambient cold exposure amplifies the effect of ambient PM1 on blood pressure and hypertensive disorders of pregnancy among Chinese pregnant women: A nationwide cohort study

BackgroundLittle evidence exists regarding the combined effect between ambient temperature and air pollution exposure on maternal blood pressure (BP) and hypertensive disorders of pregnancy (HDP). ObjectivesTo assess effect modification by temperature exposure on the PM1-BP/HDP associations among Chinese pregnant women based on a nationwide study. MethodsWe conducted a cross-sectional country-based population study in China, enrolling 86,005 participants from November 2017 to December 2021. BP was measured with standardized sphygmomanometers. HDP was defined according to the American College of Obstetricians and Gynecologists' recommendations. Daily temperature data were obtained from the European Centre for Medium-Range Weather Forecasts. PM1 concentrations were evaluated using generalized additive model. Generalized linear mixed models were used to examine the health effects, controlling for multiple covariates. We also performed a series of stratified and sensitivity analyses. ResultsThe pro-hypertensive effect of PM1 was observed in the first trimester. Cold exposure amplifies the first-trimester PM1-BP/HDP associations, with adjusted estimate (aβ) for systolic blood pressure (SBP) of 3.038 (95 % CI: 2.320–3.755), aβ for diastolic blood pressure (DBP) of 2.189 (95 % CI: 1.503–2.875), and aOR for HDP of 1.392 (95 % CI: 1.160–1.670). Pregnant women who were educated longer than 17 years or living in urban areas appeared to be more vulnerable to the modification in the first trimester. These findings remained robust after sensitivity analyses. ConclusionsFirst trimester maybe the critical exposure window for the PM1-BP/HDP associations among Chinese pregnant women. Cold exposure amplifies the associations, and those with higher education level or living in urban areas appeared to be more vulnerable.

Temporal and Spatial Analysis of Particulate Matter Concentrations in Iraq

Particulate Matter (PM) is microscopic particles of solid or liquid matter suspended in the air. The term aerosol commonly refers to the particulate/air mixture, as opposed to the particulate matter alone. Sources of particulate matter can be natural or anthropogenic. They have impacts on climate and precipitation that adversely affect human health, in ways additional to direct inhalation. The statistical methods used in this study depend on the daily, monthly, and seasonal means of PM1, PM2.5, and PM10 with data taken from the European Center for Medium-Range Weather Forecasts (ECMWF) for the year 2021 at the times of 00.00 am, and 12.00 pm over Rutba, Mosul, Basrah, and Baghdad Stations. It has been shown that particulate matter 10 μm is more abundant in the atmosphere, especially at Rutba station. It has a large vertical movement over a wide area and thus leads to an increase in temperature and thus the formation of convection currents. This type of storm occurs within a certain layer of the atmosphere, starting from the surface of the earth to about 1.52 km. These storms are severe during the day and decrease in intensity during the night. They are long-lasting and can last for two weeks.

Characteristics of Winter Precipitation over Pakistan and Possible Causes during 1981–2018

Winter (December to March) precipitation is the major source of rainfed agriculture, storage, and perennial water flow in the western river system of Pakistan. Hence, this study uses precipitation data and variables of land–ocean and atmosphere from the Pakistan Meteorological Department and European Centre for Medium-Range Weather Forecasts (ECMWF) and fifth-generation reanalysis data (ERA5), respectively, to investigate the changes in winter precipitation and its sensitivity to different land–ocean and atmosphere variables, which are rarely investigated in Pakistan. Non-parametric techniques, such as the modified Mann–Kendal, Sen slope, kernel density-based probability function (PDF), empirical orthogonal function (EOF), and correlation analysis, were used to assess the changes and modes of variability in winter precipitation. The overall seasonal precipitation showed a significant decreasing trend with a (−0.1 mm d−1 yr−1) in the seasonal mean and monthly precipitation, except in February which showed a significant increase (>0.11 mm d−1 yr−1). The highest decrease in daily precipitation (<−0.1 mm d−1 yr−1) was in the north, with a moderate decrease in the southeast. The extreme precipitation indices exhibited an erratic decreasing tendency, but the maximum daily precipitation index increased; post-2000 precipitation extremes displayed an increase, and the seasonal and monthly precipitation exhibited the highest deviations during the drought period (1995–2000). The leading precipitation mode (EOF1) is sensitive to the local land surface processes and significantly correlated (>0.60) with the central Pacific and Indian Ocean’s basin-wide sea surface temperature, corroborating the influence of ENSO-induced meridional/zonal deviation of Hadley–Walker circulations. The Hadley and Walker cells affect the south-westerlies’ jet stream strength, impacting the water vapor transport and precipitation over Pakistan. These changes in the precipitation magnitude will affect rain-fed agriculture, especially the Rabi cropping pattern and perennial river flow.

Characterization of icing conditions using aircraft reports and satellite data

A sample of 115 aircraft icing events in the Western Europe and Northeastern Atlantic sector, identified in aircraft pilot reports (PIREPs), is assessed in terms of spatio-temporal distribution and cloud characteristics, using satellite observations and products. Within our database, most of the reported events occur between October and February, although a few cases were identified during late spring and even summer months. Icing conditions are generally reported at mid-troposphere, with 82.7% and 78.6% of moderate and severe icing, respectively, identified between FL100 and FL250 (≈ 3–7.6 km). Satellite observations allow the identification of icing-prone conditions and also provide an independent means of validating some of the data in the aircraft reports. It is shown that icing occurs mainly within opaque clouds, with ice cloud-tops, or mixed phase (ice and water), noting the severe events, take place mostly under mixed-phase cloud-tops. Accordingly, most events were associated with middle and high opaque clouds, with 10.8μm brightness temperatures (BT10.8) between −40 and − 8 °C. Moreover, a detailed analysis of three events, making use of model, satellite and synoptic observations, illustrates the occurrence of aircraft icing in precipitating clouds. Within those cases, one occurred below the upper layer of a thick nimbostratus associated with an occluded low centred in the Bay of Biscay. The second event, reported as severe, as in the former case, took place within low clouds over southern England in association with northwesterly winds driven by a complex low. Finally, a moderate event happened over southern Portugal in association with a cold front, near the cloud top of nimbostratus. In all cases, the icing index operational at the Portuguese Weather Service, based on the European Centre for Medium-range Weather Forecasts (ECMWF) model, was able to predict prone-icing conditions, with higher severity in the severe cases.

Temporal Variability of Particulate Matter and Black Carbon Concentrations over Greater Cairo and Its Atmospheric Drivers

This study examines the seasonal and annual variability of black carbon (BC) and particular matter (PM1, 2.5, and 10) over Greater Cairo (Egypt) from 2003 to 2020. Data on PM and BC concentrations were gathered from the European Center for Medium-Range Weather Forecasts (ECWMF) reanalysis dataset. The Observed data from Abbasiya Station were employed to verify the accuracy of the reanalysis data, and a good agreement on both seasonal and annual scales was suggested. The results demonstrate that the highest concentrations of PM and BC were generally witnessed during wintertime, while the lowest was observed in summer. Trend analysis showed that, apart from fall, a significant increase (p < 0.05) in PM levels was evident in all seasons from 2003 to 2020. Contrarily, BC concentrations exhibited a statistically significant decline from 2003 to 2020. Moreover, BC concentrations correlated negatively with PM (1, 2.5 and 10) in all seasons and annually. However, weak and statistically non-significant (p > 0.05) correlations were found between PM and BC concentrations during wintertime. Additionally, this study looked at the atmospheric configurations corresponding to the most anomalous positive and negative phases of air pollution concentrations over Greater Cairo. The results indicate that the increase in PM concentrations is related to the positive anomalies observed at 250 hPa, which suggest a prevalence of stable atmospheric conditions, particularly during winter. Overall, our findings can serve as a foundation for improved urban planning and more effective strategies to lessen the negative effects of air quality in the largest megacity in Africa and the Middle East.

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.

Characteristics of Marine Heatwaves in the Indonesian Waters during the PDO, ENSO, and IOD Phases and Their Relationships to Net Surface Heat Flux

We conducted an investigation into the characteristics of marine heatwaves (MHWs) in Indonesian waters, aiming to understand the underlying mechanisms responsible for their formation, particularly the ones generated by net surface heat flux. To accomplish this, we utilized remote sensing data from the National Oceanic and Atmospheric Administration and the European Centre for Medium-Range Weather Forecasts. The dataset covered a 40-year period (1982–2021) encompassing both warm (1982–2007) and cold (2008–2021) phases of the Pacific Decadal Oscillation (PDO). Statistical analysis methods were employed to process the data. Our study reveals significant findings regarding MHWs in Indonesian waters. We observed the highest average frequency and maximum intensity of MHWs, occurring approximately 2–3 times a year and exceeding 1.5 °C, respectively, during the warm phase of PDO (with El Niño events occurring more frequently than La Niña). Conversely, the longest durations of MHWs in Indonesian waters were recorded during the cold phase of PDO (with La Niña events occurring more frequently than El Niño), spanning approximately 7–15 days. We identified local forcing in the form of net surface heat flux as the primary driver of MHW frequency and maximum intensity. During the warm phase of PDO, the net surface heat flux was notably higher, primarily due to increased shortwave radiation (heat gain) and reduced latent heat flux (heat loss) through the evaporation process. These factors collectively contributed to maintaining warmer ocean temperatures. Moreover, our study provides valuable insights into the interannual variability of MHWs through the application of composite calculations. We discovered a strong correlation between the occurrence of El Niño and positive Indian Ocean Dipole (IOD) events during the warm PDO phase and the highest frequency and maximum intensity of MHWs, with approximately 2.52 events and 1.54 °C, respectively. In contrast, we found that MHWs with the longest durations were closely linked to La Niña and negative IOD events during the cold PDO phase, lasting approximately 10.90 days. These findings highlight the complex interplay between climate phenomena and MHW characteristics, further deepening our understanding of their dynamics.

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.

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.

Diverging climate response of corn yield and carbon use efficiency across the U.S.

In this paper, we developed an open-source package to analyze the overall trend and responses of both carbon use efficiency (CUE) and corn yield to climate factors for the contiguous United States. Our algorithm enables automatic retrieval of remote sensing data through the Google Earth Engine (GEE) and U.S. Department of Agriculture (USDA) agricultural production data at the county level through application programming interface (API). Firstly, we integrated satellite products of net primary productivity and gross primary productivity based on the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, and climatic variables from the European Centre for Medium-Range Weather Forecasts. Secondly, we calculated CUE and commonly used climate metrics. Thirdly, we investigated the spatial heterogeneity of these variables. We applied a random forest algorithm to identify the key climate drivers of CUE and crop yield, and estimated the responses of CUE and yield to climate variability using the spatial moving window regression across the U.S. Our results show that growing degree days (GDD) has the highest predictive power for both CUE and yield, while extreme degree days (EDD) is the least important explanatory variable. Moreover, we observed that in most areas of the U.S., yield increases or stays the same with higher GDD and precipitation. However, CUE decreases with higher GDD in the north and shows more mixed and fragmented interactions in the south. Notably, there are some exceptions where yield is negatively correlated with precipitation in the Missouri and Mississippi River Valleys. As global warming continues, we anticipate a decrease in CUE throughout the vast northern part of the country, despite the possibility of yield remaining stable or increasing.