A Flood Forecasting Framework Coupling a High Resolution WRF Ensemble With an Urban Hydrologic Model

Remote sensing precipitation or precipitation from numerical weather prediction (NWP) is considered to be the best substitute for in situ ground observations for flood simulations in transboundary, data-scarce catchments. This research was aimed to evaluate the possibility of using a combination of a satellite precipitation product and NWP precipitation for better flood forecasting in the transboundary Chenab River Basin (CRB) in Pakistan. The gauge-calibrated satellite precipitation product, i.e., Global Satellite Mapping of Precipitation (GSMaP_Gauge), was selected to calibrate the Integrated Flood Analysis System (IFAS) model for the 2016 flood event in the Chenab River at the Marala Barrage gauging site in Pakistan. Precipitation from the Global Forecast System (GFS) NWP, with nine different lead times up to 4 days, was used in the calibrated IFAS model to predict the flood hydrograph in the Chenab River. The hydrologic simulations, with global GFS forecasts, were unable to predict the flood peak for all lead times. Then, the Weather Research and Forecasting (WRF) model was used to downscale the precipitation forecasts with one-way and two-way nesting approaches. In the WRF model, the CRB was centered in two domains of 25 km and 5 km resolutions. The downscaled precipitation forecasts were subsequently supplied to the IFAS model, and the predicted simulations were compared to obtain the optimal flood peak simulation in the Chenab River. It was found in this study that the simulated hydrographs, at different lead times, from the precipitation of two-way WRF nesting exhibited superior performance with the highest R2 and Nash–Sutcliffe efficiency (NSE) and the lowest percent bias (PBIAS) compared with one-way nesting. Moreover, it was concluded that the combination of GFS forecast and two-way WRF nesting can provide high-quality precipitation prediction to simulate flood hydrographs with a remarkable lead time of 96 h when applying coupled hydrometeorological flow simulation.

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.

Understanding the features of weather system around and over the Arabian Gulf is a prerequisite for setting up of a numerical weather forecasting system. The reanalysis, ERA-Interim data with a spatial resolution of ∼80 km has been utilized for analyzing the wind climatology for a period of 35 year. It is found that in the Arabian Gulf, throughout the year, the predominant wind is from north-west and the average wind speed is high in the summer between June and August. The high wind speed systems in summer are known as summer Shamals and have a typical duration of one week. Often, the high speed and long duration of the Summer Shamals makes it a good carrier of sand/dust. The winter season is dominated by south-east passage of frontal systems. The presence of land-sea contrast can cause mesoscale wind systems such as land-sea breeze. Qatar being a narrow peninsular region with width less than 100 km, the setting up of these land-sea breeze systems can be from either of the sides. This can lead to the interaction of two sea breezes from opposite sides, which may lead to the development of a convergence zone. This complex systems can affect the local weather and air-quality of coastal areas. These mesoscale phenomena and complexities are not well represented through a global model. Though the low resolution (80 km) data (ERA-interim) shows the presence of Shamal winds, the data misses the detail structure and their spatial variability. This necessitates for setting up of a high resolution mesoscale simulation approach.A non-hydrostatic mesoscale model, Weather Research and Forecast (WRF) developed by NCAR (Skamarok et al 2008) is used here for simulating and forecasting the wind systems associated with the Arabian Gulf. The model is run on an operational basis to forecast the wind fields, for a 48 hr. The modeling suite consists of a preprocessor, WPS, the model, WRF and a post processor, ARWpsot. The total suite has been compiled and installed successfully using FORTRAN and C compilers. A two way nested WRF domains with a grid size ratio of 1:3 is configured. The resolutions for the outer domain and inner domain are set to 9 km, and 3 km respectively. Various physical parametrization schemes are available within WRF modelling system. The physical parameterization schemes used in the current configuration includes MYJ boundary layer parametrization, Janjic Eta Monin–Obukhov surface layer scheme, Dudhia shortwave radiation, Rapid Radiative Transfer Model (RRTM) long wave radiation, WRF Single-Moment 6-Class (WSM6) microphysics, and the NOAH land surface scheme. Cumulus scheme, Grell is used only for the outer domain. Both of the model domains have a total of 39 vertical levels, with the topmost level at 50 hPa and lowermost level at approximately 30 m above the ground level. The initial and boundary conditions are obtained from the National Centers for Environmental Prediction's (NCEP) Global forecast System (GFS) available at resolution 0.50 (∼50 km), and the boundary conditions are updated at 6 hr interval. The entire process is automatized and run on RAAD Linux HPC cluster computing system.A two day simulation of WRF is analyzed and the sea breeze system of Qatar peninsula is examined and compared with the automatic weather station (AWS) data of Qatar Meteorological Department. The time of onset and duration of sea breeze are identified. The simulation of WRF clearly shows the development of a thermal internal boundary layer over the coastal city Doha. The existence of a convergence zone with high vertical updraft was also found. This may be due to the interaction of sea breezes coming from opposite sides of Qatar peninsula. Understanding the system of sea breeze would benefit the overall understanding pollutant transport and the dispersion mechanisms.

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.

Assimilation of accurate external satellite-based water vapor data has been demonstrated to be able to improve the Numerical Weather Prediction (NWP) model’s forecasting performance. In this study, two types of near-infrared Precipitable Water Vapor (PWV) data from the Ocean and Land Colour Instrument (OLCI) of the Sentinel-3A and -3B satellites are assimilated into the Weather Research and Forecasting (WRF) model to enhance the weather forecasting performance over the South China area. One type is the raw PWV products obtained directly from the Sentinle-3A and -3B satellites; the other type is the Sentinel-3A and -3B PWV calibrated by PWV from Global Navigation Satellite System (GNSS). The Back Propagation Neural Network (BPNN) model is used in the PWV calibration. Correspondingly, two WRF assimilation schemes are adopted to assimilate the Sentinel-3 raw PWV and Sentinel-3 calibrated PWV. Additionally, the WRF without data assimilation scheme is used as the background run to investigate the impact of data assimilation. Three WRF experimental schemes are performed for two periods, i.e., February 2020 (dry period) and July 2020 (wet period). The forecasting performance of these three WRF schemes are evaluated by observations from GNSS, radiosonde, and surface meteorological stations. The comparison results with GNSS PWV show that assimilation of Sentienl-3 calibrated PWV can improve PWV forecasting accuracy by 5.6% for February period for the first 6 h after data assimilation. Evaluated by radiosonde profiles, the assimilation of Sentinel-3 both raw and calibrated PWV can improve WRF humidity and temperature forecasting accuracy over the July period. The accuracy improvement can be up to 9.2% (at 2 km to 3 km of altitude) for water vapor mixing ratio and 6.0% for temperature (at 4 km to 5 km of altitude). The WRF rainfall evaluation result has been validated by rainfall observations from meteorological stations and it shows that the assimilation of Sentinel-3 PWV can improve the rainfall forecasting performance over the July period. The rainfall forecasting success rates of three schemes of assimilating no data, Sentinel-3 raw PWV, and Sentinel-3 calibrated PWV are 69.4%, 69.6%, and 71.1%, respectively, over the July 2020 in the South China region.

<p>The moisture recycling is defined as the fraction of precipitation over a delimited region that comes from the evaporation over that region. Its importance lies in the fact that it is an approximated measurement of a regional feedback between the atmosphere and the surface. Thus, this study estimates the spatio-temporal distribution of moisture recycling over the Iberian Peninsula (IP), and focuses on the impact of the use of 3DVAR data assimilation during the modeling stage.</p><p>For that purpose, two different simulations were run using the Weather and Research Forecasting (WRF) model with a horizontal resolution of 15 km over the IP. The first simulation (WRF N) was nested inside ERA-Interim as usual in numerical downscaling exercises, with information passed to the domain through the boundaries. The second run (WRF D) presents the same configuration as WRF N, but it also includes 3DVAR data assimilation step every six hours (at 00, 06, 12 and 18 UTC). Sea surface temperature was updated daily, and observations in PREPBUFR format included in the NCEP ADP Global Upper Air and Surface Weather Observations dataset were used for the data assimilation step. Only those inside a 120-minute window centered at the analysis times were assimilated. Both simulations cover the period 2010-2014, but the experiment WRF D was extended later until 2018.</p><p>The lowest values of moisture recycling (around 3 %) are obtained from November to February, while the most remarkable values are observed in spring (around 16 %) in both simulations. The moisture recycling is confined to the southeastern corner of the IP during winter. However, during spring and summer, a gradient of higher values towards the northeastern corner of the IP are observed in both simulations. The differences between simulations, associated to the dryness of the soil in the model, are highlighted during summer and autumn. WRF D presents a lower bias and produces more reliable results because of a better representation of the atmospheric moisture.</p><p>A Cross-Correlation Function (CCF) based analysis was performed for each combination of moisture recycling, accumulated precipitation and mean soil moisture over the IP. For the common period (2010-2014), the results show that the WRF D experiment extends the lifespan of moisture over the IP. The CCF analysis for soil moisture against precipitation also shows an unphysical negative lag (-1 month) for WRF N, whilst for WRF D both variables are simultaneous. For the extended WRF D simulation (2010-2018), it was found that the delay between precipitation and moisture recycling over the IP is five months.</p>

Long‐term variations in the frequencies of different rainfall events during summer monsoon season (June through September) are analysed over a period of 38‐years (1980–2017) using the simulations of weather research and forecasting (WRF) model evaluated against the India Meteorological Department (IMD). Further, we have divided the study period into two sub‐periods such as before (1980–1998), and after the mega ENSO event (1999–2017). Our analysis suggests the WRF exhibits good skill in capturing the increased trends in the frequency of heavy and very heavy (extreme) rainfall events during the recent epoch (1999–2017).We have assessed the skill of WRF in capturing the observed probability distributions of daily rain rates over different sub‐regions of India. Our results depict that the downscaled products of WRF captured well the extreme rainfall as observed in IMD, signifying that this dynamically downscaled model with continuous 36‐hr re‐initialization makes it suitable to simulate extreme rainfall events compared to light rainfall event. However, our analysis reports that there is a slight overestimation in WRF rainfall and fails in capturing the light rainfall events compared to IMD. The overestimation of rainfall by WRF possibly be due to increased pressure gradients and enhances the convection in the upper streams and associated rainfall. Our study emphasizes that the variability in summer monsoon rainfall during the recent period is due to the changes in the monsoon circulations, particularly monsoon low‐level jet that leads to alter the moisture budget in terms of moisture convergence and transport. Moreover, the variability in frequencies of rainfall events are analysed for strong La Niña/El Niño years and observed an increase in extremes over major part of India during strong La Niña except over parts of Central India and NEI where strong El Niño dominates; which is well captured by the WRF model.

The present study focuses on the assessment of urban heat island (UHI) and urban energy fluxes over Indian metropolitan cities. For this purpose, a high-resolution numerical Weather Research and Forecasting (WRF) model and a coupled single-layer urban canopy model (WRF-UCM) has been implemented over Delhi, Kolkata and Hyderabad to evaluate the performance of these models in capturing the impact of urban areas in simulating the UHI over different land use land cover (LULC) classes. The model initial conditions are obtained from NCEP-FNL reanalysis data. The model simulations reveal that the air temperature at 2 m height (T2) and UHI intensity are overestimated by WRF whereas the WRF-UCM simulations are matching well with ERA5-Land reanalysis data considered as observations. For the WRF-UCM simulation, the statistical errors in T2 and UHI intensity are within the desirable limits and the index of agreement (IoA) is more than 0.8 over the built-up areas (BAs). By comparing with the satellite observations, the relative surface UHI is noticed to be better captured over the BA by WRF-UCM simulation. The sensible heat flux simulated by WRF is highly overestimated whereas WRF-UCM is in good agreement with the satellite observation over the BA. Interestingly, the latent heat flux is reasonably well simulated over all the LULC classes. The results derived from the present study have shown the performance of WRF-UCM in simulating the spatial variation of UHI and fluxes of sensible and latent heat-related varied LULC classifications over metropolitan cities of India.

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 paper evaluates microphysics and cumulus parameterization schemes of the Weather Research and Forecasting (WRF) model for high-resolution weather forecasting in tropics. WRF forecasts at 5-km resolution between 8 and 9 hours after initial time for 5 representative storm systems over Thailand and nearby regions for 24 different combinations of microphysics and cumulus parameterization schemes are evaluated using brightness temperatures coincidentally observed by the passive millimeter-wave spectrometer Advanced Microwave Sounding Unit (AMSU) aboard the U.S. National Oceanic and Atmospheric Administration (NOAA)-16 satellite and corresponding retrieved precipitation parameters from the AMSU MIT Precipitation retrieval (AMP) products. Results show that forecasts are very sensitive to microphysics and cumulus parameterization schemes. The combination of the WRF Double-Moment 6-class microphysics scheme and the Betts-Miller-Janjic cumulus parameterization scheme performs best. By using the best physics combination, WRF forecasts generally agree with AMSU observations in terms of morphologies, locations, and intensities. Main forecast errors include storm location offsets and under-forecasting of graupel.

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.

A summertime convective event that developed on 5 September 2017 over the United Arab Emirates (UAE) is investigated in this study. Atmospheric profiles from a ground‐based microwave radiometer along with satellite observations and in situ data from three weather stations in the UAE were used. The event was simulated using the Weather Research and Forecasting (WRF) model, forced with four input datasets: Global Forecast System (GFS), Climate Forecast System Reanalysis (CFSR), and the European Centre for Medium‐Range Weather Forecasts ERA‐5 and ERA‐Interim reanalyses. The afternoon and evening convection was triggered by the intrusion of a mid‐level trough from midlatitudes and was favoured by the convergence over land of thermally forced maritime air masses. Near‐surface observations at a weather station revealed a 7 °C drop in air temperature, a doubling of the wind speed to 9 m·s−1 in 30 min, and a shift in wind direction from easterly, to southerly and then westerly in about 45 min, associated with the passage of the cold pool emanating from a Mesoscale Convective System (MCS). At the location where the microwave radiometer was deployed, the pre‐squall low and wake low signatures are also captured, with a 5 m·s−1 increase in the wind speed in just 5 min. The observed features of the studied MCS were found to compare with those reported for MCSs in the Tropics. The four experiments gave a similar performance, although the GFS simulation generally generated higher skill scores. The investigated MCS event was not captured by WRF, which was attributed to a misrepresentation of soil moisture in the model. This study highlights the difficulties regional models like WRF may have in reproducing MCSs over arid/hyperarid regions, which may result in a misrepresentation of their impacts in climate projection studies.

Recent studies have shown the unique value of satellite-observed land surface thermal infrared (TIR) information (e.g., skin temperature) and the feasibility of assimilating land surface temperature (LST) into land surface models (LSMs) to improve the simulation of land-atmosphere water and energy exchanges. In this study, two different types of LST assimilation techniques are implemented and the benefits from the techniques are compared. One of the techniques is to directly assimilate LST using ensemble Kalman filter (EnKF) data assimilation (DA) utilities. The other is to use the Atmosphere-Land Exchange Inversion model (ALEXI) as an “observation operator” that converts LST retrievals into the soil moisture (SM) proxy based on the ratio of actual to potential evapotranspiration (fPET), which is then assimilated into an LSM. While most current studies have shown some success in both directly the assimilating LST and assimilating ALEXI SM proxy into offline LSMs, the potential impact of the assimilation of TIR information through coupled numerical weather prediction (NWP) models is unclear. In this study, a semi-coupled Land Information System (LIS) and Weather Research and Forecast (WRF) system is employed to assess the impact of the two different techniques for assimilating the TIR observations from NOAA GOES satellites on WRF model forecasts. The NASA LIS, equipped with a variety of LSMs and advanced data assimilation tools (e.g., the ensemble Kalman Filter (EnKF)), takes atmospheric forcing data from the WRF model run, generates updated initial land surface conditions with the assimilation of either LST- or TIR-based SM and returns them to WRF for initializing the forecasts. The WRF forecasts using the daily updated initializations with the TIR data assimilation are evaluated against ground weather observations and re-analysis products. It is found that WRF forecasts with the LST-based SM assimilation have better agreement with the ground weather observations than those with the direct LST assimilation or without the land TIR data assimilation.

A sensitivity study was developed with Lin, Morrison 2-moment, weather research and forecasting (WRF) single-moment 5-class (WSM5), and WRF single-moment 6-class (WSM6) microphysics schemes available in the weather research and forecasting-advanced research WRF (WRF-ARW) for the numerical forecast of the wind field at José Martí International Airport, in Cuba. The selection of these schemes was based on their use in numerical weather forecast systems operating in Cuba. As case studies, five storms associated with synoptic patterns that cause dangerous conditions at this aerodrome were selected. The simulations were initialized at 0000 UTC with the forecast outputs of the global forecast system (GFS) model. The schemes were evaluated according to the wind field’s representation in the region where the airport is located, the headlands, and the center of the runway. The errors observed are strongly dependent on the occurrence of convection, especially on the intensity and the factors that cause it. During the dry season (November–April), the lowest errors are observed, while the worst performance is appreciable for the rainy period (May–October). Lin and WSM6 schemes reproduce the best behavior of the wind field on the aerodrome.

This paper investigates the accuracy of weather research and forecasting by improving coding for solar radiation forecasting for location in Dili Timor Leste. Weather Research and Forecasting (WRF) model version 3.9.1 is used in this study for improvement purposes. The shortwave coding of WRF is used to improve in order to decrease error simulation. The importance of improving WRF coding at a specific region will reduce the bias and root mean square root when comparing to the observed data. This study uses high resolution based on the WRF modeling to stabilize the performance of forecasting. The decrease in error performance will be expected to enhance the value of renewable energy. The results show the root mean square error of the WRF default is 233 W/m2 higher compared to 205 W/m2 from the WRF improvement model. In addition, the Mean Bias Error (MBE) of the WRF default is obtained value 0.06 higher than 0.03 from the WRF improvement in rainy days. Meanwhile, on sunny days, the performance Root Mean Square Error (RMSE) of WRF default is 327 W/m2 higher than 223 W/m2 from the WRF improvement. The MBE of WRF improvement obtained 0.13 lower compared to 0.21 of WRF default coding. Finally, this study concludes that improving the shortwave code under the WRF model can decrease the error performance of the WRF simulation for local weather forecasting.