A Numerical Weather Forecasting System for Qatar

Kaiga, located on the west coast of Karnataka, India is a complex terrain region with dense forest cover often influenced by land-sea breeze flows during warm summer season. In this study, dispersion of tritium released from Kaiga Nuclear Power Plant (NPP) under the influence of land-sea breeze circulations is simulated using FLEXPART-WRF models. Sensitivity experiments with Weather Research and Forecasting (WRF) are conducted with two Land Surface Models (LSMs), Noah and Noah-Multi Physics (Noah-MP) to study their impact on the wind flow and meteorological characteristics. Mini Boundary Layer Masts (MBLMs) and Doppler SODAR observations are used for evaluating the model performance. WRF simulations showed large sensitivity of simulated sea breeze characteristics to the model land surface physics. It has been found that the Noah-MP better represented the onset time, inland extent, intensity and duration of the sea breeze circulation over the Kaiga complex terrain region with lesser error statistics compared to Noah. The improvements with Noah-MP over Noah are due to the better representation of vegetation and soil hydrological processes over the dense forest region, realistic simulation of surface energy and momentum fluxes and reduction in the land-sea temperature contrast by including a separate canopy physics. FLEXPART simulation of tritium releases from the Kaiga NPPs using WRF predicted meteorological fields showed large diurnal variability in the dispersion pattern and air concentrations around the Kaiga valley. Tritium plume is transported to the northwest sector during early morning and morning time by the land-breeze and to the east-southeast sector by the sea breeze during daytime. Predicted tritium concentrations are found to be higher during the morning under stable atmospheric conditions and lower during sea breeze hours due to stronger winds and deep mixed layers. FLEXPART predicted tritium activity concentrations at different measurement locations using WRF-Noah-MP meteorological data are in better agreement with measurements with higher correlation coefficient and index of agreement. • Tritium dispersion over a coastal complex terrain site is modeled by using the coupled WRF-FLEXPART model. • Dispersion of tritium released from a Nuclear Power Plant under mesoscale sea breeze and land breeze circulations is presented. • Sensitivity of Land Surface Models, Noah and Noah-MP in WRF on the simulation of sea breeze characteristics and tritium dispersion are assessed. • Explicit treatment of forest canopy in the Noah-MP LSM over a model domain with dense forest improved the model results.

Impact of different reanalysis data on WRF dynamical downscaling over China

In this work, precipitable water vapour (PWV), its diurnal variability and water vapour vertical profiles have been obtained from Weather Research and Forecasting (WRF) model simulations. The PWV simulated values have been validated using observations from the Global Navigation Satellite Systems (GNSS) for three locations in an orographically complex region, the Canary Islands archipelago, while the vertical water vapour profiles have been compared with radiosonde data available for this region. Initial and boundary conditions for simulations are given by National Center for Environmental Prediction (NCEP) global models: FNL (Global Final Analysis) and GFS (Global Forecast System). Several parametrizations and assumptions, related to cloud microphysics, boundary‐layer and land surface models, number of vertical levels, moisture transport and 3D nudging techniques, have been applied to evaluate the sensitivity of the WRF results. In general, a good agreement between WRF and GNSS data was obtained, with correlation coefficients larger than 0.95 and centred root mean square differences about 2 mm. Furthermore, for PWV and water vapour vertical distribution, no appreciable differences were observed between the different experiments carried out, since the main contribution to water vapour in a region with small land coverage comes from the large‐scale processes, which are reasonably represented when analysis data are used as the forcing boundary conditions of the model. The ability of the WRF simulations to reproduce the PWV diurnal cycle was also verified. Additionally, the skills of the WRF simulations to determine PWV were evaluated for different nudging strategies when 48 h forecast simulations were performed; these showed insignificant differences.

This study is intended to simulate and better understand mean and extreme precipitation over the Loess Plateau (LP) in China using the Weather Research and Forecasting (WRF) model. We performed a long-term (1980–2011) simulation with the WRF model at a 10 km horizontal resolution, forced by the ERA-Interim reanalysis. A series of sensitivity experiments were conducted to investigate the influence of resolution and physical parameterization schemes on the simulation ability of the WRF model over the LP. The region has a dominant semi-arid climate and is adversely affected by extreme precipitation. Results show that the WRF model produced better simulations at a 10 km resolution for the LP with complex terrain than it did at coarser resolutions. WRF simulations for precipitation over the LP were most sensitive to cumulus schemes, moderately sensitive to planetary boundary layer schemes, and less sensitive to microphysics schemes. The WRF model not only adequately captured the spatial distribution of precipitation in the LP but also reproduced its magnitude and variability at different time scales. Moreover, the WRF model captured 73% of the observed extreme precipitation events with an intensity of more than 12 mm day− 1, which cause substantial soil erosion in the LP. Although it was unable to capture 27% of the observed events, the WRF model reasonably reproduced ERA-Interim extreme precipitation and its dynamic processes. These analyses strongly suggest that the biased extreme precipitation generated with the WRF model most likely resulted from inaccurate ERA-Interim forcing data. This study provides a better understanding of extreme precipitation simulations as well as insight into predictions of soil erosion in the LP.

In this study, National Centers for Environmental Prediction (NCEP) Final (FNL) operational global analysis data and meteorological observation data from 2013 to 2017 were used to evaluate the impact of seasonal changes and different circulation classifications on the dynamical downscaling simulation results of Weather Research and Forecasting (WRF) in the Pearl River Delta (PRD) region. The results show that the dynamical downscaling method can accurately simulate the time variation characteristics of the near-surface meteorological field and the hit rates of a 2-m temperature, 2-m relative humidity, 10-m wind speed, and 10-m wind direction are 92.66%, 93.98%, 26.78%, and 76.78%, respectively. The WRF model slightly underestimates the temperature and relative humidity, and overestimates the wind speed and precipitation. For precipitation, the WRF model can better simulate the variation characteristics of light rain and heavy rain, with the probability of detection are 0.59 and 0.69, respectively. For seasonal factors, the WRF model can conduct a perfect simulation in autumn and winter, followed by spring, while summer is vulnerable to extreme weather, so the result of the simulation is relatively poor. The circulation type is an important parameter of downscaling assessment. When the PRD is controlled by high pressure, the simulated results of WRF are good, and when the PRD is affected by low pressure or extreme weather, the simulation results are relatively poor.

Artificial intelligence (AI) models have demonstrated advancements in computational efficiency and forecast accuracy relative to the Numerical Weather Prediction (NWP), but they are unable to fully represent high-dimensional atmospheric dynamics. Thus, some AI-NWP coupled frameworks have been proposed, such as integrating AI-driven boundary conditions with numerical models to leverage the strengths of both approaches. However, in this coupled framework, ensemble forecasts and associated error propagation and energy dynamics remain under-explored. In this study, an AI-NWP coupled system that also uses the stochastic kinetic energy backscatter scheme (SKEBS) to generate ensemble forecasts is established. Ensemble simulations of Typhoon Yutu (2018) are carried out with the Weather Research and Forecasting (WRF) model employing Pangu-Weather and FuXi forecast data as boundary forcing. The results show that the ensemble WRF_Pangu (WRF_FuXi) improved Yutu’s track forecast by 67% (50%) compared to the traditional physics-based WRF_GFS (Global Forecast System), and reduced its intensity underestimation by about 67% relative to their AI global counterparts. Nonetheless, WRF_FuXi and WRF_Pangu exhibited limited ensemble spread and linear error growth, reflecting deterministic tendencies. Comparison of global and regional experiments show that Pangu-Weather is more physically constrained and thus better aligned with the WRF model for regional applications, while the adaptation of FuXi to the regional model is less robust. Spectral analysis revealed that AI-derived boundaries introduced excessive small-scale energy and underestimated larger-scale energy. The regional model WRF acted as a “conveyor belt”, propagating additive small-scale energy upscale, ultimately overwhelming the stochastic perturbations for ensemble generation. These findings underscore the need to incorporate more physical features into the AI-derived boundary conditions for ensemble forecasting.

The spectral method is generally assumed to provide better numerical accuracy than the finite difference method. However, the majority of regional models use finite discretization methods due to the difficulty of specifying time-dependent lateral boundary conditions in spectral models. This study evaluates the behavior of nonhydrostatic dynamics with a spectral discretization. To this end, Juang’s nonhydrostatic dynamical core for the National Centers for Environmental Prediction (NCEP) regional spectral model has been implemented into the Regional Model Program (RMP) of the Global/Regional Integrated Model system (GRIMs). The behavior of the nonhydrostatic RMP is validated, and compared with that of the hydrostatic core in 2-D idealized experiments: the mountain wave, rising thermal bubble, and density current experiments. The nonhydrostatic effect in the RMP is further validated in comparison with the results from the Weather Research and Forecasting (WRF) model, which uses a finite difference method. The analyses of the experimental results from the RMP generally follow the characteristics found in previous studies without any discernible difference. For example, in both the RMP and the WRF model, the eastward-tilted propagation of mountain waves is very similar in the nonhydrostatic core experiments. Both nonhydrostatic models also efficiently reproduce the motion and deformation of the warm and cold bubbles, but the RMP results contain more small-scale noise. In a 1-km real-case simulation testbed, the lee waves that originate over the eastern flank of the Korean peninsula travel further eastward in the WRF model than in the RMP. It is found that differences of small-scale wave characteristics between the RMP and WRF model are mainly from the numerical techniques used, such as the accuracy of the advection scheme and the magnitude of the numerical diffusion, rather than from discrepancies in the spatial discretization.

ABSTRACTAn operational wave forecast system for the area of Galway Bay is presented. Three numerical models are implemented: Weather Research and Forecasting (WRF), Wavewatch III (WWIII) and Simulating Waves Nearshore (SWAN). GFS (Global Forecast System) winds, from NCEP (National Centres for Environmental Prediction), are used to force the WWIII model, while WRF winds are used to force the SWAN model. Bathymetries for WWIII and SWAN were obtained from NOAA’s GEODAS (Geophysical Data System) data base and from GEBCO (General Bathymetric Chart of the Oceans), hosted by BODC. The wave generation model, WWIII, is used to simulate waves for the whole North Atlantic, whereas the SWAN model runs for the Irish west coast, with a nested grid for Galway Bay. Forecast products of 7 days are produced daily, with a warm up of 4 days. The validations of the forecast system were made for both wind and wave parameters with data from buoys located in the Irish coast and inside Galway Bay. Comparisons in the time domain as well as a statistical analysis were performed. Overall, the wave forecast system gave good results closely reproducing the real data, with a low error.

Abstract. Meteorological observations over the Tibetan Plateau (TiP) are scarce, and precipitation estimations over this remote region are difficult. The constantly improving capabilities of numerical weather prediction (NWP) models offer the opportunity to reduce this problem by providing precipitation fields and other meteorological variables of high spatial and temporal resolution. Longer time periods of years to decades can be simulated by NWP models by successive model runs of shorter periods, which can be described by the term "regional atmospheric reanalysis". In this paper, we assess the Weather Research and Forecasting (WRF) models capacity in retrieving rain- and snowfall on the TiP in such a configuration using a nested approach: the simulations are conducted with three nested domains at spatial resolutions of 30, 10, and 2 km. A validation study is carried out for a one-month period with a special focus on one-week (22–28 October 2008), during which strong rain- and snowfall was observed on the TiP. The output of the model in each resolution is compared to the Tropical Rainfall Measuring Mission (TRMM) data set for precipitation and to the Moderate Resolution Imaging Spectroradiometer (MODIS) data set for snow extent. TRMM and WRF data are then compared to weather-station measurements. Our results suggest an overall improvement from WRF over TRMM with respect to weather-station measurements. Various configurations of the model with different nesting and forcing strategies, as well as physical parameterisation schemes are compared to propose a suitable design for a regional atmospheric reanalysis over the TiP. The WRF model showed good accuracy in simulating snow- and rainfall on the TiP for a one-month simulation period. Our study reveals that there is nothing like an optimal model strategy applicable for the high-altitude TiP, its fringing high-mountain areas of extremely complex topography and the low-altitude land and sea regions from which much of the precipitation on the TiP is originating. The choice of the physical parameterisation scheme will thus be always a compromise depending on the specific purpose of a model simulation. Our study demonstrates the high importance of orographic precipitation, but the problem of the orographic bias remains unsolved since reliable observational data are still missing. The results are relevant for anyone interested in carrying out a regional atmospheric reanalysis. Many hydrological analyses and applications like rainfall-runoff modelling or the analysis of flood events require precipitation rates at daily or even hourly intervals. Thus, our study offers a process-oriented alternative for retrieving precipitation fields of high spatio-temporal resolution in regions like the TiP, where other data sources are limited.

Repeat-pass spaceborne interferometric synthetic aperture radar (InSAR) is commonly used to measure surface deformation; phase delays due to atmospheric water vapour may have significant impact on the accuracy of these measurements. In recent years, there has been a growing interest in using forecasts and analyses from numerical weather prediction (NWP) models – which can provide good estimates of the atmospheric state – to correct for atmospheric phase delays. In this study, three separate estimates of atmospheric water vapour content from NWP output are used in combination with Environmental Satellite (Envisat) Advanced Synthetic Aperture Radar (ASAR) data over the Pearl River Delta region in South China to mitigate atmospheric distortion. The NWP-based estimates are derived from: (1) interpolation of National Centers for Environmental Prediction (NCEP) Final Operational Global Analysis (FNL) data; (2) Weather Research and Forecasting (WRF) model simulations initialized with FNL analysis without additional data assimilation; and (3) WRF simulations initialized with a three-dimensional variational (3DVar) data assimilation system that ingests additional meteorological observations. The accuracy of the atmospheric corrections from these different NWP model outputs is further verified quantitatively with precipitable water vapour (PWV) data from several ground-based global positioning system (GPS) stations in Hong Kong. Inter-comparison shows a good agreement between the PWV derived from the WRF-3DVar simulations and the GPS measurements, suggesting that atmospheric correction by convection-permitting WRF simulations initialized with mesoscale data assimilation may effectively mitigate atmospheric distortion in InSAR measurements, especially for coastal areas.

This study aims to assess the impacts of assimilating the clear-sky radiances from the Advanced Geosynchronous Radiation Imager (AGRI) onboard the Fengyun-4A (FY-4A) satellite on 72-h forecasts. First, we compare the water vapour (WV) brightness temperature (TB) in July 2018 from the National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS) analyses and Weather Research and Forecasting (WRF) forecasts with the clear-sky AGRI WV channel observations. The results suggest that the NCEP GDAS analyses are more consistent with the AGRI observations than the WRF forecasts, and the AGRI observations can be utilized to improve the initial conditions of the WRF model by data assimilation, especially in water vapour and surface temperature. After the preliminary verification, two identical cycling assimilation experiments are performed with and without the AGRI WV TB assimilation for a month. The results reveal that the WRF forecasts with AGRI TB assimilation are closer to satellite observations than the first guess in both WV channels. The validation with WV channel data of the Microwave Humidity Sounder (MHS) and SAPHIR sensors indicates that after the AGRI data assimilation, the errors are smaller than in the control experiment (without AGRI assimilation). The assimilation of the TB observed by AGRI WV channels shows a remarkable positive impact on moisture forecasts at middle and upper levels. Comparison of the TB from the WRF forecasts with the MHS observations suggests that the AGRI WV channel assimilation can improve the predictions. Overall, assimilating AGRI WV observations can positively influence the WRF analyses and forecasts.

This paper verifies the strengths and positions of extratropical cyclones around North America and the adjacent oceans within the Short Range Ensemble Forecast (SREF) system at the National Centers for Environmental Prediction (NCEP) during the 2004–07 cool seasons (October–March). The SREF mean for cyclone position and central pressure has a smaller error than the various subgroups within SREF and the operational North American Mesoscale (NAM) model in many regions on average, but not the operational Global Forecast System (GFS) for many forecast times. Inclusion of six additional Weather Research and Forecasting (WRF) model members into SREF during the 2006–07 cool season did not improve the SREF mean predictions. The SREF has slightly more probabilistic skill over the eastern United States and western Atlantic than the western portions of the domain for cyclone central pressure. The SREF also has slightly greater probabilistic skill than the combined GFS and NAM for central pressure, which is significant at the 90% level for many regions and thresholds. The SREF probabilities are fairly reliable, although the SREF is overconfident at higher probabilities in all regions. The inclusion of WRF did not improve the SREF probabilistic skill. Over the eastern Pacific, eastern Canada, and western Atlantic, the SREF is overdispersed on average, especially early in the forecast, while across the central and eastern United States the SREF is underdispersed later in the forecast. There are relatively large biases in cyclone central pressure within each SREF subgroup. As a result, the best-member diagrams reveal that the SREF members are not equally accurate for the cyclone central pressure and displacement. Two cases are presented to illustrate examples of SREF developing large errors early in the forecast for cyclones over the eastern United States.

Abstract. Italy is a territory characterized by complex topography with the Apennines mountain range crossing the entire peninsula and its highest peaks in central Italy. Using the latter as our area of interest and the snow seasons 2018/19, 2019/20 and 2020/21, the goal of this study is to investigate the ability of a simple single-layer and a more sophisticated multi-layer snow cover numerical model to reproduce the observed snow height, snow water equivalent and snow extent in the central Apennines, using for both models the same forecast weather data as meteorological forcing. We here consider two well-known ground surface and soil models: (i) Noah LSM, an Eulerian model which simulates the snowpack as a bulk single layer, and (ii) Alpine3D, a multi-layer Lagrangian model which simulates the snowpack stratification. We adopt the Weather Research and Forecasting (WRF) model to produce the meteorological data to drive both Noah LSM and Alpine3D at a regional scale with a spatial resolution of 3 km. While Noah LSM is already online-coupled with the WRF model, we develop here a dedicated offline coupling between WRF and Alpine3D. We validate the WRF simulations of surface meteorological variables in central Italy using a dense network of automatic weather stations, obtaining correlation coefficients higher than 0.68, except for wind speed, which suffered from the model underestimation of the real elevation. The performances of both WRF–Noah and WRF–Alpine3D are evaluated by comparing simulated and measured snow height, snow height variation and snow water equivalent, provided by a quality-controlled network of automatic and manual snow stations located in the central Apennines. We find that WRF–Alpine3D can predict better than WRF–Noah the snow height and the snow water equivalent, showing a correlation coefficient with the observations of 0.9 for the former and 0.7 for the latter. Both models show similar performances in reproducing the observed daily snow height variation; nevertheless WRF–Noah is slightly better at predicting large positive variations, while WRF–Alpine3D can slightly better simulate large negative variations. Finally we investigate the abilities of the models in simulating the snow cover area fraction, and we show that WRF–Noah and WRF–Alpine3D have almost equal skills, with both models overestimating it. The equal skills are also confirmed by Jaccard and the average symmetric surface distance indices.

Numerical weather prediction (NWP) models such as the Weather Research and Forecasting (WRF) model are increasingly used over the Indian region to forecast extreme rainfall events. However, studies which explore the application of high-resolution rainfall simulations obtained from the WRF model in urban hydrology are limited. In this paper, the utility of a model coupling framework to predict urban floods is explored through the case study of Bangalore city in India. This framework is used to simulate multiple extreme events that occurred over the city for the monsoons of years 2020 and 2021. To address the uncertainty from the WRF model, a 12-member convection permitting ensemble is used. Model configurations using Kain Fritsch and WSM6 parameterization schemes could simulate the spatial and temporal pattern of the selected event. The city is easily flooded with rainfall events above a threshold of 60 mm/day and to capture the response of the urban catchment, the Personal Computer Storm Water Management Model (PCSWMM) is used in this study. Flood forecasts are created using the outputs from the WRF ensemble and the Global Forecasting System (GFS). The high temporal and spatial resolution of the rainfall forecasts (<4 km at 15-min intervals), has proved critical in reproducing the urban flood event. The flood forecasts created using the WRF ensemble indicate that flooding and water levels are comparable to the observed whereas the GFS underestimates these to a large extent. Thus, the coupled WRF–PCSWMM modelling framework is found effective in forecasting flood events over an Indian city.

To date, various satellite observations have been assimilated in numerical weather prediction (NWP) , making a dramatic contribution to the improvement of forecast accuracy. However, the focus has mainly been on satellite measurements in a clear atmosphere, with relatively little attention paid to exploring the use of satellite observations under cloudy and rainy conditions. One of the key issues related to cloud and precipitation data assimilation is the radiative effect of hydrometeors in the rapid radiative transfer model (RRTM ). This paper presents a detailed assessment of the radiative effect of hydrometeors in the RRTM, in support of satellite cloud and precipitation microwave data assimilation. Using the output of hydrometeors from the Weather Research and Forecasting (WRF ) numerical forecast model as the input for the Community Radiative Transfer Model (CRTM) , the radiative effect of hydrometeors on the simulation of Advanced Microwave Sounding Unit (A and B) (AMSU-A and AMSU-B , respectively) satellite observations are analyzed. Then, the sensitivity of the satellite simulation to hydrometeors’ properties, including the water content, particle size and vertical distribution, are investigated. Finally, the result of CRTM is compared and discussed with that of Radiative Transfer for TOVS (RTTOV )—another popular RRTM used in the numerical weather prediction community. The results show that the inclusion of the radiative effect of hydrometeors in the RRTM makes the satellite brightness temperature simulation match up, reasonably successfully, with the observation. Overall, the radiative effects of hydrometeors have diverse influences in most of the channels of the satellite microwave observations, except the AMSU-A high-level temperature sounding channels 10–14. Investigation of the radiative effect of the individual hydrometeors verifies that the cloud and rain water mainly have a warming effect, owing to radiative emission. This effect dominates three of the AMSU-A window channels, 1–3, but is weakened both in the other AMSU-A window channel, 15, and in the AMSU-B window channel 1. The effect of cloud and rain water in the other channels is one of scattering, which decreases the brightness temperature. Ice, snow and graupel all present a cooling effect, owing to scattering. The variation produced by ice is extremely small, but is obvious in AMSU-B. The effect of both snow and graupel is notable in all AMSU-A and AMSU-B channels. The sensitivity of satellite microwave remote sensing to the water content of hydrometeors corresponds well with their radiative effect. The brightness temperature is not sensitive to the effective radius size of cloud and ice, and the sensitivity of satellite observations to the particle size of rain, snow and graupel is strong. Also, the sensitivity is complicated by the frequency. The sensitivity of the satellite simulation to the vertical distribution of hydrometeors is presented by the transfer of the particular channel affected. Additionally, the results of RTTOV and CRTM are generally consistent. The main discrepancy is the magnitude of the response function of hydrometeors and the corresponding deviation of brightness temperature produced by the radiative effect of hydrometeors. The result in CRTM appears to be at least double the magnitude of that in RTTOV.