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Amidst growing concerns over climate-induced extreme weather events, precise flood forecasting becomes imperative, especially in regions like the Chaersen Basin where data scarcity compounds the challenge. Traditional hydrologic models, while reliable, often fall short in areas with insufficient observational data. This study introduces a hybrid modeling approach that combines the deep learning capabilities of the Informer model with the robust hydrological simulation by the WRF-Hydro model to enhance runoff predictions in such data-sparse regions. Trained initially on the diverse and extensive CAMELS dataset in the United States, the Informer model successfully applied its learned insights to predict runoff in the Chaersen Basin, leveraging transfer learning to bridge data gaps. Concurrently, the WRF-Hydro model, when integrated with The Global Forecast System (GFS) data, provided a basis for comparison and further refinement of flood prediction accuracy. The integration of these models resulted in a significant improvement in prediction precision. The synergy between the Informer's advanced pattern recognition and the physical modeling strength of the WRF-Hydro significantly enhanced the prediction accuracy. The final predictions for the years 2015 and 2016 demonstrated notable increases in the Nash–Sutcliffe Efficiency (NSE) and the Index of Agreement (IOA) metrics, confirming the effectiveness of the hybrid model in capturing complex hydrological dynamics during runoff predictions. Specifically, in 2015, the NSE improved from 0.5 with WRF-Hydro and 0.63 with the Informer model to 0.66 using the hybrid model, while in 2016, the NSE increased from 0.42 to 0.76. Similarly, the IOA in 2015 rose from 0.83 with WRF-Hydro and 0.84 with the Informer model to 0.87 using the hybrid approach, and in 2016, it increased from 0.78 to 0.92. Further investigation into the respective contributions of the WRF-Hydro and the Informer models revealed that the hybrid model achieved the optimal performance when the contribution of the Informer model was maintained between 60%-80%. [ABSTRACT FROM AUTHOR]

The tropospheric delay is difficult to be modeled accurately resulting from the high variability of atmospheric water vapor, especially under the conditions of sparse station distribution and large elevation differences, which poses challenges for real-time precise positioning. In this contribution, a real-time high-resolution (0.01° × 0.01°) zenith tropospheric delay (ZTD) model considering sparse stations and topography variations (named GFNSS) is established by integrating the information from the Global Forecast System (GFS) and Global Navigation Satellite System (GNSS). GNSS observations and GFS forecasts in the Hong Kong area are selected for the experiments. The performance of ZTDs derived from GFNSS is assessed and validated with the real-time GNSS ZTDs obtained by the precise point positioning method and the IGS post-processed ZTD products. Results show that the root mean square error (RMSE) of GFNSS ZTDs is 5.5 mm and 12.8 mm when validated with real-time and post-processed ZTD, while those for ZTD derived from the low-order surface model (LSM) are 8.8 mm and 19.0 mm, presenting a reduction of 37.5% and 32.6%, respectively. The sensitivity of model performance to the number of modeling stations and elevation differences is also evaluated. The results reveal that the GFNSS model is resistant to station number and presents high accuracy and stability with the RMSE values varying between 4.0 and 6.0 mm as the modeling station number decreases from 13 to 4, while the RMSE for the LSM model increases dramatically from 4.0 to 27.4 mm. Meanwhile, the GFNSS model achieves an RMSE value of 5.8 mm when the elevation differences are over 300 m, indicating a notable 84.9% reduction compared to that of LSM (RMSE of 38.5 mm). [ABSTRACT FROM AUTHOR]

Abstract Amidst growing concerns over climate-induced extreme weather events, precise flood forecasting becomes imperative, especially in regions like the Chaersen Basin where data scarcity compounds the challenge. Traditional hydrologic models, while reliable, often fall short in areas with insufficient observational data. This study introduces a hybrid modeling approach that combines the deep learning capabilities of the Informer model with the robust hydrological simulation by the WRF-Hydro model to enhance runoff predictions in such data-sparse regions. Trained initially on the diverse and extensive CAMELS dataset in the United States, the Informer model successfully applied its learned insights to predict runoff in the Chaersen Basin, leveraging transfer learning to bridge data gaps. Concurrently, the WRF-Hydro model, when integrated with The Global Forecast System (GFS) data, provided a basis for comparison and further refinement of flood prediction accuracy. The integration of these models resulted in a significant improvement in prediction precision. The synergy between the Informer’s advanced pattern recognition and the physical modeling strength of the WRF-Hydro significantly enhanced the prediction accuracy. The final predictions for the years 2015 and 2016 demonstrated notable increases in the Nash–Sutcliffe Efficiency (NSE) and the Index of Agreement (IOA) metrics, confirming the effectiveness of the hybrid model in capturing complex hydrological dynamics during runoff predictions. Specifically, in 2015, the NSE improved from 0.5 with WRF-Hydro and 0.63 with the Informer model to 0.66 using the hybrid model, while in 2016, the NSE increased from 0.42 to 0.76. Similarly, the IOA in 2015 rose from 0.83 with WRF-Hydro and 0.84 with the Informer model to 0.87 using the hybrid approach, and in 2016, it increased from 0.78 to 0.92. Further investigation into the respective contributions of the WRF-Hydro and the Informer models revealed that the hybrid model achieved the optimal performance when the contribution of the Informer model was maintained between 60%-80%.

European Space Agency (ESA) launched its first space-based Doppler Wind Lidar (DWL) mission called Atmospheric Dynamic Mission (ADM) – Aeolus. Onboard the Aeolus mission is the Atmospheric LAser Doppler Instrument (ALADIN) which measures the horizontal line-of-sight (HLOS) winds. Aeolus Level-2B wind observations in Rayleigh clear and Mie cloudy channels are evaluated for implementation in the NCMRWF (National Centre for Medium Range Weather Forecasting) Global Forecast System (NGFS). The GSI (grid-point statistical interpolation) analysis scheme has been modified and updated to assimilate the HLOS wind information. Quality control criteria are applied during observation processing and during minimization of cost function for computation of the initial condition. An observation system experiment (OSE) is performed by employing the GSI-3DVar (3-Dimensional Variation) approach and involving HLOS data. In addition to assimilation and forecast diagnostics, two case studies of very severe cyclonic storms are investigated to assess the impact of this new wind information on a severe weather event. Statistically, significant improvement is observed mostly over the Southern Hemisphere, Tropics, and RSMC (Regional Specialized Meteorological Centre, 29°–120°E and 21°S–46°N) region in terms of reduction in wind root mean square error. Assimilation of HLOS winds shows a reduction in direct positional error (DPE) for both cyclonic systems. Improvement in the 6-hourly analysis of minimum sea level pressure and maximum 10 m wind speed is also observed. [ABSTRACT FROM AUTHOR]

Herein, we present the initial application of the NOAA-EPA Atmosphere-Chemistry Coupler (NACC) in the cloud ("NACC-Cloud", version 1), which processes NOAA's operational Global Forecast System version 16 (GFSv16) meteorology on-demand and produces model-ready meteorological files needed to drive U.S. EPA's Community Multiscale Air Quality (CMAQ) model. NACC is adapted from the U.S. EPA's Meteorology-Chemistry Interface Processor version 5 (MCIPv5) and is used as the primary model coupler in the current operational NWS/NOAA air quality forecasting model. The development and use of NACC-Cloud in this work are critical to provide the scientific community streamlined access to NOAA's operational GFSv16 data and user-defined processing and download of model-ready, meteorological input for any regional CMAQ domain worldwide. The NACC-Cloud system was implemented on the Amazon® Web Services High-Performance Computing platform, and results from this work show that the NACC-Cloud system is immediately beneficial to the air quality modeling community worldwide. [ABSTRACT FROM AUTHOR]

Prediction of Total Cloud Cover (TCDC) from numerical weather simulation models, such as Global Forecast System (GFS), can aid renewable energy engineers in monitoring and forecasting solar photovoltaic power generation. A major challenge is the systematic bias in TCDC simulations induced by the errors in the numerical model parameterization stages. Correction of GFS-derived cloud forecasts at multiple time steps can improve energy forecasts in electricity grids to bring better grid stability or certainty in the supply of solar energy. We propose a new kernel ridge regression (KRR) model to reduce bias in TCDC simulations for medium-term prediction at the inter-daily, e.g., 2–8 day-ahead predicted TCDC values. The proposed KRR model is evaluated against multivariate recursive nesting bias correction (MRNBC), a conventional approach and eight machine learning (ML) methods. In terms of the mean absolute error (MAE), the proposed KRR model outperforms MRNBC and ML models at 2–8 day ahead forecasts, with MAE ≈ 20–27%. A notable reduction in the simulated cloud cover mean bias error of 20–50% is achieved against the MRNBC and reference accuracy values generated using proxy-observed and non-corrected GFS-predicted TCDC in the model's testing phase. The study ascertains that the proposed KRR model can be explored further to operationalize its capabilities, reduce uncertainties in weather simulation models, and its possible consideration for practical use in improving solar monitoring and forecasting systems that utilize cloud cover simulations from numerical weather predictions. [Display omitted] • KRR model for bias correction of Total Cloud Cover forecasts is proposed. • KRR reduces bias in two-to-eight-day ahead forecasts at 0, 3, 6 UTC. • Cloud cover forecast error is reduced between 20-50% against conventional error reduction methods. • KRR is validated with multivariate recursive nested bias correction and other models. • KRR reduces bias in Total Cloud Cover forecasts for solar energy forecast systems. [ABSTRACT FROM AUTHOR]

Herein, we present the initial application of the NOAA-EPA Atmosphere-Chemistry Coupler (NACC) in the cloud (“NACC-Cloud”, version 1), which processes NOAA’s operational Global Forecast System version 16 (GFSv16) meteorology on-demand and produces model-ready meteorological files needed to drive U.S. EPA’s Community Multiscale Air Quality (CMAQ) model. NACC is adapted from the U.S. EPA’s Meteorology-Chemistry Interface Processor version 5 (MCIPv5) and is used as the primary model coupler in the current operational NWS/NOAA air quality forecasting model. The development and use of NACC-Cloud in this work are critical to provide the scientific community streamlined access to NOAA’s operational GFSv16 data and user-defined processing and download of model-ready, meteorological input for any regional CMAQ domain worldwide. The NACC-Cloud system was implemented on the Amazon® Web Services High-Performance Computing platform, and results from this work show that the NACC-Cloud system is immediately beneficial to the air quality modeling community worldwide.

• A deep-learning model was proposed to improve the rain forecasts of the Global Forecast System. • The corrected rain grids improved the flood forecasting by WRF-Hydro. • The attention module was found to play an important role in rain forecasting. With the intensification of climate change and global warming, the risk of extreme rain events is rising, underscoring the importance of the accurate flood forecasting especially for the data-sparse regions like the Northeast China. Presently, physics-based, distributed hydrologic models were extensively utilized for flood simulations. However, the Global Forecasting System (GFS) forcing, as one of the most common sources of global rain forecasts, still had considerable room for improvement in local accuracy. In response to this challenge, the Attention-Enhanced Meteorological Prediction Generative Adversarial Network (AEMP-GAN) model was developed based on deep learning to enhance the local rainfall predictions. WRF-Hydro was adopted to test the impact of the corrected rain forecasts on flood simulations. The results indicate that after applying the AEMP-GAN model to enhance the 24-hour GFS forecasts, the average root mean square error (RMSE) of the rain forecasts during the flood season was reduced by 28.7%, and the index of agreement (IOA) was raised by 11.3%. When this improved rain forecasts were used to drive the WRF-Hydro model to simulate runoff for the three typical rain events during August-September 2021, the Nash-Sutcliffe Efficiency (NSE) coefficients of the simulated runoff at the outlet of the studied watershed for the three events increased by 8.42%, 4.98%, and 1.01%, respectively, while the RMSE decreased by 4.53%, 3.54%, and 0.55%, respectively. At a different stream gauge near the middle of the watershed, the NSE improved for two events by 23.96% and 3.90%, respectively, owing to the corrected rain forecasts alone, while the RMSE dropped by 20.1% and 7.86%, respectively. These findings demonstrate the significant potential of the proposed method on tuning the global rain forecasts for improving the accuracy of the local flood forecasting. [ABSTRACT FROM AUTHOR]

Medium-range (1–15 day) precipitation forecasts are increasingly available from global weather models. This study presents evaluation of the Global Forecast System (GFS) for the Volta river basin in West Africa. The evaluation was performed using two satellite-gauge merged products: NASA’s Integrated Multi-satellitE Retrievals (IMERG) “Final Run” satellite-gauge merged rainfall observations, and the University of California Santa Barbara’s Climate Hazard’s group Infrared Precipitation with Stations (CHIRPS). The performance of GFS depends on the climate zone, with underestimation bias in the dry Sahel climate, overestimation bias in the wet Guinea Coastal climate, and relatively no bias in the moderately wet Savannah climate. Averaging rainfall over the watershed of the Akosombo dam (i.e., averaging across all three climate zones), the GFS forecast indicates low skill (Kling-Gupta Efficiency KGE = 0.42 to 0.48) for the daily, 1-day, lead GFS forecast, which deteriorates further as the lead time increases. A sharp decrease in KGE occurred between 6 to 10 days. Aggregating the forecasts over long timescales improves the accuracy of the GFS forecasts. On a 15-day accumulation timescale, GFS shows higher skills (KGE = 0.74 to 0.88).

The Korea Institute of Atmospheric Prediction Systems (KIAPS) began a national project to develop a new global atmospheric model system in 2011. The ultimate goal of this 9-year project is to replace the current operational model at the Korea Meteorological Administration (KMA), which was adopted from the United Kingdom’s Meteorological Office’s unified model (UM) in 2010. The 12-km Korean Integrated Model (KIM) system, consisting of a spectral-element non-hydrostatic dynamical core on a cubed sphere grid and a state-of-the-art physics parameterization package, has been launched in a real-time forecast framework, with initial conditions obtained via the advanced hybrid four-dimensional ensemble variational data assimilation (4DEnVar) over its native grid. A development strategy for KIM and the evolution of its performance in medium-range forecasts toward a world-class global forecast system are described. Outstanding issues in KIM 3.1 as of February 2018 are discussed, along with a future plan for operational deployment in 2020. [ABSTRACT FROM AUTHOR]

On 17th June 2013 the state of Uttarakhand in India (Latitude 28.72°N to 31.45°N and Longitude 77.57°E–81.03°E) received more than 340 mm of rainfall, which is 375% more than the daily normal (65.9 mm) rainfall during monsoon. This caused heavy floods in Uttarakhand as well as unprecedented damage to life and property. In this study we aim at assessing the performance of two deterministic forecast models, Global Forecast System (GFS/T574) and Unified Model (NCUM), run at NCMRWF, in predicting the heavy rainfall observed over Uttarakhand region of India during 17–18th June, 2013. Verification of the synoptic features in forecasts of the two models suggests that NCUM accurately captures the circulation features as compared to T574. Further verification of this event is carried out based on the contiguous rain area (CRA) technique. CRA verification is used in computing the total mean square error (MSE) which is based on displacement, volume and pattern errors. This verification technique also, confirms the better skill of NCUM over T574 in terms of forecast peak rainfall amounts, volume and average rain rate, lower MSE and root mean square error (RMSE) as well as having higher hit rates and lower misses and false alarm rates for different rainfall thresholds from Day 1 to Day 5 forecasts.